From 7833f8c265d5d3a38c607047d917a8b60d02b3ff Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Thu, 3 Jul 2025 18:07:51 +0530 Subject: [PATCH 1/8] active learning loop workflow on custom dataset --- .../flowers/Test/1392131677_116ec04751.jpg | Bin 0 -> 83038 bytes .../flowers/Test/1955336401_fbb206d6ef_n.jpg | Bin 0 -> 31605 bytes .../flowers/Test/2346726545_2ebce2b2a6.jpg | Bin 0 -> 69008 bytes src/deepforest/data/flowers/flowers.ipynb | 4336 +++++++++++ src/deepforest/data/flowers/ground_truth.json | 1 + .../0b8942ea-162362897_1d21b70621_m.jpg | Bin 0 -> 20286 bytes .../0cf6ab9b-14147016029_8d3cf2414e.jpg | Bin 0 -> 108350 bytes .../images/0ea84707-5794839_200acd910c_n.jpg | Bin 0 -> 22855 bytes src/deepforest/data/flowers/labels.csv | 398 + src/deepforest/data/flowers/labels_raw.csv | 398 + src/deepforest/data/flowers/preds.json | 1 + src/deepforest/data/flowers/result.json | 6761 +++++++++++++++++ .../data/flowers/val_vis/img_103.png | Bin 0 -> 147123 bytes 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I tried other models but they were taking too much time and resources, I felt it was more ideal for me to use a very light model just making a basic loop.\n", + "\n", + "Once my loop feels decent, I would change to a heavier and more accurate model\n" + ] + }, + { + "cell_type": "markdown", + "id": "e51bb0c7", + "metadata": {}, + "source": [ + "What is mAP@0.5?\n", + "\n", + "- mAP = mean Average Precision, a standard detection metric\n", + "\n", + "- AP at a single IoU threshold (0.5) = area under the precision–recall curve when a predicted box is “correct” if IoU ≥ 0.5\n", + "\n", + "- mean over classes (here only one “daisy” class) and averaged across images" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "250151f2", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import random\n", + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader, Subset\n", + "from torchvision.models.detection import fasterrcnn_mobilenet_v3_large_320_fpn\n", + "from torchvision.transforms import functional as F, Resize\n", + "from pycocotools.coco import COCO\n", + "from pycocotools.cocoeval import COCOeval\n", + "from PIL import Image, ImageDraw\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15d4169e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Label map: {0: 1}\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", + " warnings.warn(msg)\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Round 1/5, train=3\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Round 2/5, train=4\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + "Round 3/5, train=5\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + "Round 4/5, train=6\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Round 5/5, train=7\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference done → test_results/\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# ---- Settings ----\n", + "TRAIN_JSON = \"result.json\" \n", + "TRAIN_DIR = \"images\" \n", + "TEST_DIR = \"Test\" \n", + "CSV_FILE = \"labels.csv\"\n", + "GT_JSON = \"ground_truth.json\"\n", + "DEVICE = torch.device(\"cpu\") \n", + "\n", + "RESIZE = Resize((320, 320))\n", + "BATCH_SIZE = 1\n", + "INITIAL = 3\n", + "VAL_SIZE = 2\n", + "POOL_BATCH = 1\n", + "ROUNDS = 5\n", + "\n", + "# ---- Dataset ----\n", + "class DaisyDataset(Dataset):\n", + " def __init__(self, csv, img_dir):\n", + " self.df = pd.read_csv(csv)\n", + " self.img_dir = img_dir\n", + " self.imgs = self.df['image_path'].unique().tolist()\n", + " def __len__(self):\n", + " return len(self.imgs)\n", + " def __getitem__(self, idx):\n", + " fn = self.imgs[idx]\n", + " img = Image.open(os.path.join(self.img_dir, fn)).convert(\"RGB\")\n", + " img = RESIZE(img)\n", + " recs = self.df[self.df['image_path'] == fn]\n", + " boxes = recs[['xmin','ymin','xmax','ymax']].values.astype(np.float32)\n", + " labels = recs['label'].values.astype(np.int64)\n", + " target = {\n", + " 'boxes': torch.from_numpy(boxes),\n", + " 'labels': torch.from_numpy(labels),\n", + " 'image_id': torch.tensor([idx])\n", + " }\n", + " return F.to_tensor(img), target\n", + "\n", + "def collate_fn(batch):\n", + " return tuple(zip(*batch))\n", + "\n", + "# ---- Parse COCO → CSV ----\n", + "def parse_coco(json_file, img_dir):\n", + " coco = COCO(json_file)\n", + " records = []\n", + " for ann in coco.loadAnns(coco.getAnnIds()):\n", + " img = coco.loadImgs(ann['image_id'])[0]\n", + " fn = os.path.basename(img['file_name'])\n", + " x,y,w,h = ann['bbox']\n", + " records.append({\n", + " 'image_path': fn,\n", + " 'xmin': x, 'ymin': y,\n", + " 'xmax': x+w, 'ymax': y+h,\n", + " 'label_orig': ann['category_id']\n", + " })\n", + " df = pd.DataFrame(records)\n", + " df.to_csv(\"labels_raw.csv\", index=False)\n", + " return df\n", + "\n", + "# ---- Build COCO GT JSON ----\n", + "def build_coco_gt(df, out_json):\n", + " images, anns, cats = [], [], []\n", + " uniq = df['image_path'].unique().tolist()\n", + " # images\n", + " for i, fn in enumerate(uniq):\n", + " images.append({\n", + " 'id': int(i),\n", + " 'file_name': fn\n", + " })\n", + " # annotations\n", + " aid = 1\n", + " for _, r in df.iterrows():\n", + " x1 = int(r['xmin'])\n", + " y1 = int(r['ymin'])\n", + " x2 = int(r['xmax'])\n", + " y2 = int(r['ymax'])\n", + " w = x2 - x1\n", + " h = y2 - y1\n", + " anns.append({\n", + " 'id': int(aid),\n", + " 'image_id': int(uniq.index(r['image_path'])),\n", + " 'category_id': int(r['label']),\n", + " 'bbox': [x1, y1, w, h],\n", + " 'area': float(w * h),\n", + " 'iscrowd': 0\n", + " })\n", + " aid += 1\n", + " # categories\n", + " for cid in sorted(df['label'].unique()):\n", + " cats.append({\n", + " 'id': int(cid),\n", + " 'name': str(cid)\n", + " })\n", + " coco_dict = {\n", + " 'info': {},\n", + " 'licenses': [],\n", + " 'images': images,\n", + " 'annotations': anns,\n", + " 'categories': cats\n", + " }\n", + " # write out\n", + " with open(out_json, 'w') as f:\n", + " json.dump(coco_dict, f)\n", + " return COCO(out_json)\n", + "\n", + "\n", + "# ---- Training & Eval ----\n", + "def train_epoch(model, opt, loader, device):\n", + " model.train()\n", + " for imgs, tgts in loader:\n", + " imgs = [i.to(device) for i in imgs]\n", + " tgts = [{k:v.to(device) for k,v in t.items()} for t in tgts]\n", + " losses = model(imgs, tgts)\n", + " loss = sum(losses.values())\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + "@torch.no_grad()\n", + "def evaluate_map(model, loader, device, coco_gt):\n", + " model.eval()\n", + " preds = []\n", + " for imgs, tgts in loader:\n", + " imgs = [i.to(device) for i in imgs]\n", + " outs = model(imgs)\n", + " for tgt, out in zip(tgts, outs):\n", + " img_id = int(tgt['image_id'].item())\n", + " for box, score, label in zip(out['boxes'].cpu(), out['scores'].cpu(), out['labels'].cpu()):\n", + " x1,y1,x2,y2 = box.tolist()\n", + " preds.append({\n", + " 'image_id': img_id,\n", + " 'category_id': int(label),\n", + " 'bbox': [x1,y1,x2-x1,y2-y1],\n", + " 'score': float(score)\n", + " })\n", + " if not preds:\n", + " print(\"→ No detections, mAP@0.5=0.0\")\n", + " return 0.0\n", + " with open(\"preds.json\",\"w\") as f:\n", + " json.dump(preds, f)\n", + " coco_dt = coco_gt.loadRes(\"preds.json\")\n", + " ev = COCOeval(coco_gt, coco_dt, iouType=\"bbox\")\n", + " ev.params.imgIds = sorted(coco_gt.getImgIds())\n", + " ev.evaluate(); ev.accumulate(); ev.summarize()\n", + " return ev.stats[1]\n", + "\n", + "# ---- Main ----\n", + "def main():\n", + " # 1) parse COCO → raw labels\n", + " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", + " # 2) remap labels → \"Daisy\" -> 1 \n", + " mapping = {orig: i+1 for i,orig in enumerate(df['label_orig'].unique())}\n", + " print(\"Label map:\", mapping)\n", + " df['label'] = df['label_orig'].map(mapping)\n", + " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", + " # 3) build COCO GT\n", + " coco_gt = build_coco_gt(df, GT_JSON)\n", + "\n", + " # 4) prepare splits\n", + " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", + " n_classes = df['label'].nunique() + 1\n", + " idxs = list(range(len(dataset)))\n", + " random.shuffle(idxs)\n", + " train_idx = idxs[:INITIAL]\n", + " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", + " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", + "\n", + " # 5) model & optimizer\n", + " model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", + " pretrained=False,\n", + " pretrained_backbone=True,\n", + " num_classes=n_classes\n", + " ).to(DEVICE)\n", + " opt = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", + "\n", + " # 6) active learning loop\n", + " perf, sizes = [], []\n", + " for r in range(ROUNDS):\n", + " print(f\"Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", + " tr_loader = DataLoader(Subset(dataset, train_idx), batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", + " val_loader= DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False,collate_fn=collate_fn)\n", + "\n", + " train_epoch(model, opt, tr_loader, DEVICE)\n", + " mAP50 = evaluate_map(model, val_loader, DEVICE, coco_gt)\n", + " perf.append(mAP50); sizes.append(len(train_idx))\n", + "\n", + " if pool_idx:\n", + " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", + " train_idx += add\n", + " pool_idx = [i for i in pool_idx if i not in add]\n", + " else:\n", + " break\n", + "\n", + " # 7) plot\n", + " plt.plot(sizes, perf, marker='o')\n", + " plt.xlabel(\"# Training Images\"); plt.ylabel(\"mAP@0.5\")\n", + " plt.title(\"Daisy Active Learning\"); plt.grid(True)\n", + " plt.show()\n", + "\n", + " # 8) inference on TEST_DIR\n", + " os.makedirs(\"test_results\", exist_ok=True)\n", + " model.eval()\n", + " for fn in sorted(os.listdir(TEST_DIR)):\n", + " if not fn.lower().endswith((\".jpg\",\".png\")): continue\n", + " img = Image.open(os.path.join(TEST_DIR,fn)).convert(\"RGB\")\n", + " img = RESIZE(img)\n", + " t = F.to_tensor(img).to(DEVICE)\n", + " out = model([t])[0]\n", + " draw = ImageDraw.Draw(img)\n", + " for box, score in zip(out['boxes'].cpu(), out['scores'].cpu()):\n", + " if score < 0.5: continue\n", + " x1,y1,x2,y2 = box.tolist()\n", + " draw.rectangle([x1,y1,x2,y2], outline=\"red\", width=2)\n", + " draw.text((x1,y1-10), f\"{score:.2f}\", fill=\"red\")\n", + " img.save(os.path.join(\"test_results\", fn))\n", + " print(\"Inference done → test_results/\")\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ] + }, + { + "cell_type": "markdown", + "id": "9e61717e", + "metadata": {}, + "source": [ + "# Next Steps\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8b9e363", + "metadata": {}, + "source": [ + "\n", + "\n", + "- Plot a longer active learning curve\n", + " - Run more rounds (e.g., up to 20–30 samples used), andplot mAP vs training size. That’s when performance shouldstart climbing meaningfully.\n", + "- Try a baseline using all 131 images\n", + " - Train the model using more data and evaluate — this givesyou an upper bound on achievable mAP@0.5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9616c0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.01s)\n", + "creating index...\n", + "index created!\n", + "Label map: {0: 1}\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", + " warnings.warn(msg)\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Round 1/28, train=3\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.03s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " mAP@0.5 = 0.0001\n", + "Round 2/28, train=4\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 3/28, train=5\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 4/28, train=6\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 5/28, train=7\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 6/28, train=8\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 7/28, train=9\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 8/28, train=10\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0050\n", + "Round 9/28, train=11\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 10/28, train=12\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 11/28, train=13\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 12/28, train=14\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " mAP@0.5 = 0.0025\n", + "Round 13/28, train=15\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " mAP@0.5 = 0.0025\n", + "Round 14/28, train=16\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " mAP@0.5 = 0.0033\n", + "Round 15/28, train=17\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 16/28, train=18\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " mAP@0.5 = 0.0033\n", + "Round 17/28, train=19\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0050\n", + "Round 18/28, train=20\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0050\n", + "Round 19/28, train=21\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0017\n", + "Round 20/28, train=22\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0050\n", + "Round 21/28, train=23\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " mAP@0.5 = 0.0099\n", + "Round 22/28, train=24\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0025\n", + "Round 23/28, train=25\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " mAP@0.5 = 0.0033\n", + "Round 24/28, train=26\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0017\n", + "Round 25/28, train=27\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " mAP@0.5 = 0.0033\n", + "Round 26/28, train=28\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " mAP@0.5 = 0.0025\n", + "Round 27/28, train=29\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 28/28, train=30\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline training on full dataset...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", + " warnings.warn(msg)\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Baseline epoch 1/5\n", + " Baseline epoch 2/5\n", + " Baseline epoch 3/5\n", + " Baseline epoch 4/5\n", + " Baseline epoch 5/5\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Baseline mAP@0.5 on validation set: 0.0099\n", + "Inference done → test_results/\n" + ] + } + ], + "source": [ + "# ---- Settings ----\n", + "TRAIN_JSON = \"result.json\" \n", + "TRAIN_DIR = \"images\" \n", + "TEST_DIR = \"Test\" \n", + "CSV_FILE = \"labels.csv\"\n", + "GT_JSON = \"ground_truth.json\"\n", + "DEVICE = torch.device(\"cpu\") \n", + "\n", + "RESIZE = Resize((320, 320))\n", + "BATCH_SIZE = 1\n", + "INITIAL = 3\n", + "VAL_SIZE = 2\n", + "POOL_BATCH = 1\n", + "ROUNDS = 28 # run up to ~30 training samples\n", + "\n", + "\n", + "# ---- Main ----\n", + "def main():\n", + " # 1) parse COCO → raw labels\n", + " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", + " # 2) remap labels → 1…N\n", + " mapping = {orig: i+1 for i,orig in enumerate(df['label_orig'].unique())}\n", + " print(\"Label map:\", mapping)\n", + " df['label'] = df['label_orig'].map(mapping)\n", + " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", + " # 3) build COCO GT\n", + " coco_gt = build_coco_gt(df, GT_JSON)\n", + "\n", + " # 4) prepare splits\n", + " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", + " n_classes = df['label'].nunique() + 1\n", + " idxs = list(range(len(dataset)))\n", + " random.shuffle(idxs)\n", + " train_idx = idxs[:INITIAL]\n", + " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", + " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", + "\n", + " # 5) model & optimizer\n", + " model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", + " pretrained=False,\n", + " pretrained_backbone=True,\n", + " num_classes=n_classes\n", + " ).to(DEVICE)\n", + " opt = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", + "\n", + " # 6) active learning loop\n", + " perf, sizes = [], []\n", + " for r in range(ROUNDS):\n", + " print(f\"Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", + " tr_loader = DataLoader(Subset(dataset, train_idx), batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", + " val_loader= DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False,collate_fn=collate_fn)\n", + "\n", + " train_epoch(model, opt, tr_loader, DEVICE)\n", + " mAP50 = evaluate_map(model, val_loader, DEVICE, coco_gt)\n", + " print(f\" mAP@0.5 = {mAP50:.4f}\")\n", + " perf.append(mAP50); sizes.append(len(train_idx))\n", + "\n", + " if pool_idx:\n", + " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", + " train_idx += add\n", + " pool_idx = [i for i in pool_idx if i not in add]\n", + " else:\n", + " break\n", + "\n", + " # 7) plot\n", + " plt.plot(sizes, perf, marker='o')\n", + " plt.xlabel(\"# Training Images\"); plt.ylabel(\"mAP@0.5\")\n", + " plt.title(\"Daisy Active Learning\"); plt.grid(True); plt.show()\n", + "\n", + " # 8) baseline on full dataset\n", + " print(\"Baseline training on full dataset...\")\n", + " base_model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", + " pretrained=False,\n", + " pretrained_backbone=True,\n", + " num_classes=n_classes\n", + " ).to(DEVICE)\n", + " base_opt = torch.optim.SGD(base_model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", + " full_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", + " for ep in range(5):\n", + " print(f\" Baseline epoch {ep+1}/5\")\n", + " train_epoch(base_model, base_opt, full_loader, DEVICE)\n", + " base_mAP = evaluate_map(\n", + " base_model,\n", + " DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn),\n", + " DEVICE, coco_gt\n", + " )\n", + " print(f\"Baseline mAP@0.5 on validation set: {base_mAP:.4f}\")\n", + "\n", + " # 9) inference on TEST_DIR\n", + " os.makedirs(\"test_results\", exist_ok=True)\n", + " model.eval()\n", + " for fn in sorted(os.listdir(TEST_DIR)):\n", + " if not fn.lower().endswith((\".jpg\",\".png\")): continue\n", + " img = Image.open(os.path.join(TEST_DIR,fn)).convert(\"RGB\")\n", + " img = RESIZE(img)\n", + " t = F.to_tensor(img).to(DEVICE)\n", + " out = model([t])[0]\n", + " draw = ImageDraw.Draw(img)\n", + " for box, score in zip(out['boxes'].cpu(), out['scores'].cpu()):\n", + " if score < 0.5: continue\n", + " x1,y1,x2,y2 = box.tolist()\n", + " draw.rectangle([x1,y1,x2,y2], outline=\"red\", width=2)\n", + " draw.text((x1,y1-10), f\"{score:.2f}\", fill=\"red\")\n", + " img.save(os.path.join(\"test_results\", fn))\n", + " print(\"Inference done → test_results/\")\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "markdown", + "id": "a5a03577", + "metadata": {}, + "source": [ + "# INTRODUCED UNCERTAINTY SAMPLINGS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e4d5ace", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Label map: {0: 1}\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", + " warnings.warn(msg)\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Round 1/14, train=3\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.03s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " mAP@0.5 = 0.0010\n", + "Round 2/14, train=4\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " mAP@0.5 = 0.0000\n", + "Round 3/14, train=5\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n", + " mAP@0.5 = 0.0011\n", + "Round 4/14, train=6\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.03s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " mAP@0.5 = 0.0010\n", + "Round 5/14, train=7\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " mAP@0.5 = 0.0012\n", + "Round 6/14, train=8\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " mAP@0.5 = 0.0099\n", + "Round 7/14, train=9\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0099\n", + "Round 8/14, train=10\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " mAP@0.5 = 0.0009\n", + "Round 9/14, train=11\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " mAP@0.5 = 0.0099\n", + "Round 10/14, train=12\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " mAP@0.5 = 0.0034\n", + "Round 11/14, train=13\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.011\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " mAP@0.5 = 0.0113\n", + "Round 12/14, train=14\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0099\n", + "Round 13/14, train=15\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " mAP@0.5 = 0.0099\n", + "Round 14/14, train=16\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.011\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " mAP@0.5 = 0.0106\n" + ] + }, + { + "data": { + "image/png": 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MiqiZWINERFctPT0dv/zyC/7zn/8gKysLM2fOlHtIpABXrlxBbGwsJk6ciNDQUJw+fRorV66EwWDAY489JvfwiBrFAImIrtrJkycxYcIEBAUFYfny5Q32CqK2xc/PD71798ZHH32EjIwMeHp6Yvjw4XjjjTcQEBAg9/CIGsUaJCIiIqJaWINEREREVAsDJCIiIqJaWIPUSmazGcnJyfD29m71LuJERERkX4IgoKCgAKGhoVCrG84TMUBqpeTk5AY7/xIREZGyXb58GWFhYQ3ezwCplby9vQFUvsA+Pj4yj4aIiIiaIz8/H+Hh4dLneEMYILWSOK3m4+PDAImIiMjBNFUewyJtIiIioloYIBERERHVwgCJiIiIqBbWINmYyWRCeXm53MMgO3N1dYVGo5F7GERE1EoMkGxEEASkpqYiNzdX7qGQTHx9fWEwGNgni4jIATFAshExOAoKCoKHhwc/JNsQQRBQXFyM9PR0AEBISIjMIyIiopZigGQDJpNJCo64Y3Xb5O7uDgBIT09HUFAQp9uIiBwMi7RtQKw58vDwkHkkJCfx/WcNGhGR42GAZEOcVmvb+P4TETkuTrEREVGbYjIL2JeQjfQCI4K8degX5Q+Nmn/QkCUGSOS0du7ciVtuuQU5OTnw9fWVezhEpADxx1OwcMtJpOQZpdtC9DosGNEVw7pxQQVV4xSbgpnMAvacz8J3R65gz/ksmMyC3EOSxaefftqqAGfgwIFISUmBXq9v9mOmTp2KUaNGtfhaRKR88cdT8Pi6QxbBEQCk5hnx+LpDiD+eItPISImYQVIo/pVT6WoKnN3c3GAwGKw4GiJyVCazgIVbTqK+PzMFACoAC7ecxG1dDZxuIwDMICmSXH/lREZGYtmyZRa39ezZEy+99BKAyqLjjz76CPfccw88PDzQuXNnbN682eL4EydO4K677oKPjw+8vb1x00034fz589L9H330Ebp06QKdTofo6Gh88MEH0n0XL16ESqXChg0bMGTIEOh0OnzxxReYNm0a8vLyoFKpoFKppPGsXbsWffr0gbe3NwwGAx544AGp9xBQOcWmUqmkZp1iJmrr1q3o0qULvLy8MGzYMKSkVL6eL730Ej777DN899130rV27tyJW2+9FTNmzLB4nhkZGXBzc8P27duv5iUnIjvZl5Bd53dqTQKAlDwj9iVk229QpGgMkOxAEAQUl1U066vAWI4Fm080+FcOALy0+SQKjOXNOp8gWHdabuHChRg7diz++usv3HnnnZgwYQKysyt/oVy5cgX/+Mc/oNVqsWPHDhw8eBAPPvggKioqAABffPEF5s+fj9deew2nTp3C66+/jhdffBGfffaZxTWef/55zJw5E6dOncItt9yCZcuWwcfHBykpKUhJScHTTz8NoDK79Morr+Do0aPYtGkTLl68iKlTpzY6/uLiYrz99ttYu3YtfvvtNyQmJkrne/rppzF27FgpaEpJScHAgQPx0EMPYf369SgtLZXOs27dOrRv3x633nqrtV5aIrKh9IKGg6PWHEfOj1NsdlBSbkLX+Vutci4BQGq+ETEv/dys40++HAcPN+u9zVOnTsX48eMBAK+//jqWL1+Offv2YdiwYXj//feh1+vx1VdfwdXVFQBw7bXXSo9dsGABlixZgnvvvRcAEBUVhZMnT+LDDz/ElClTpONmzZolHQMAer0eKpWqznTZgw8+KP1/x44dsXz5cvTt2xeFhYXw8vKqd/zl5eVYuXIlOnXqBACYMWMGXn75ZQCAl5cX3N3dUVpaanGte++9FzNmzMB3332HsWPHAqjMRk2dOpVL+YkcRJC3zqrHkfNjBolapHv37tL/e3p6wsfHR5rWOnLkCG666SYpOKqpqKgI58+fx/Tp0+Hl5SV9vfrqqxZTcADQp0+fZo3l4MGDGDFiBDp06ABvb28MGTIEAJCYmNjgYzw8PKTgCKjcBqTmtFx9dDodJk2ahE8++QQAcOjQIRw/frzJbBURKUe/KH+E6HVo7E+aEH3lkn8igBkku3B31eDky3HNOnZfQjamrtnf5HGfTuvbrH/I7q7N3+JCrVbXmZKrXSRdO/hRqVQwm82V16raXqM+hYWFAIDVq1ejf//+FvfV3obD09OzybEWFRUhLi4OcXFx+OKLLxAYGIjExETExcWhrKyswcfVN/7mTEM+9NBD6NmzJ5KSkrBmzRrceuutiIiIaPJxRKQMGrUKC0Z0xePrDjV4zDVBXmB9NokYINmBSqVq9jTXTZ0DEaLXITXPWG8dkgqAQa/DTZ0Drb7SIjAwUCpYBoD8/HwkJCQ0+/Hdu3fHZ599hvLy8jqBSHBwMEJDQ3HhwgVMmDChReNyc3ODyWSyuO306dPIysrCG2+8gfDwcADAgQMHWnTe5l4LAGJiYtCnTx+sXr0a69evx3vvvXfV1yIi+xrWLQQrJvbC7K+Poris+t+5n4crcorL8fu5THz8RwIeuqmjjKMkpeAUm8KIf+UAqJMKFr9fMKKrTZah3nrrrVi7di1+//13HDt2DFOmTGnRJqszZsxAfn4+7r//fhw4cADnzp3D2rVrcebMGQCVBd6LFi3C8uXLcfbsWRw7dgxr1qzB0qVLGz1vZGQkCgsLsX37dmRmZqK4uBgdOnSAm5sb/v3vf+PChQvYvHkzXnnllat6/uK1/vrrL5w5cwaZmZkWGbSHHnoIb7zxBgRBwD333HPV1yIi+xvWLQT9Iv0AAGP7hOHLh2/EgXm3Yd7wLgCA1348hW0n0+QcIikEAyQFEv/KMegtiwUNeh1WTOxlsz5Ic+bMwZAhQ3DXXXdh+PDhGDVqlEW9TlMCAgKwY8cOFBYWYsiQIejduzdWr14tZZMeeughfPTRR1izZg1iYmIwZMgQfPrpp4iKimr0vAMHDsRjjz2GcePGITAwEIsXL0ZgYCA+/fRTbNy4EV27dsUbb7yBt99++6qePwA8/PDDuO6669CnTx8EBgZi165d0n3jx4+Hi4sLxo8fD52OhZxEjupiVjEAYNQN7TGgUwA0ahWmD47ChP4dIAjAv748jONX8mQeJclNJVh7HXgbkZ+fD71ej7y8PPj4+FjcZzQakZCQgKioqKv6IOV+Qcpy8eJFdOrUCfv370evXr2aPN5aPwdEZD1lFWZ0mR8Pk1nAn3OGWvwhWm4y48FP9+P3c5kI9tHiuycG1/lDlRxfY5/fNTGDpGAatQoDOgVgZM/qv3LI/srLy5Gamop58+bhxhtvbFZwRETKdDmnGCazAHdXDYJ9tBb3uWrUeH9CL3QO8kJafimmf7YfRaUVMo2U5MYAiagJu3btQkhICPbv34+VK1fKPRwiugoJGUUAgKh2nvX2MfPRueKTqX3RzssNJ5LzMfOrI212H8y2jgESURNuvvlmCIKAM2fOICYmRu7hENFVSMisCpACG24nEu7vgVWT+8DNRY1fTqVh0Y+n7DU8UhAGSERE1GZcqAqQOrZrvN9arw5+WDKmBwDgoz8SsO7PSzYfGykLAyQbYv1728b3n0h5EjIrm9ZGNREgAcCIHqF4+vbK7ZIWbD6B385m2HRspCwMkGxAXNZeXFws80hITuL7X9/WK0QkD2mKrRkBEgA8ccs1uLdXe5jMAp744hDOphXYcnikIOykbQMajQa+vr7SHl8eHh7c1LQNEQQBxcXFSE9Ph6+vb4uabRKR7RSVViAtvxRA8wMklUqFRffGICmnBPsSsjFtzX5semIQAr21TT+YHBoDJBsRd4NvaiNUcl6+vr7SzwERyU/MHvl7usHXw63Zj9O6aPDhxN6454NduJhVjEfWHsCXD98IXQv2uiTHwwDJRlQqFUJCQhAUFFRnw1dyfq6urswcESlMS6fXavLzdMMnU/ving9243BiLp7eeBTL778BavanszqlNElmgGRjGo2GH5RERApwNQESAHQM9MKHk3pj0sd78f1fKYhq54n/u/06aw6xzYs/noKFW04iJc8o3Rai12HBiK4222arISzSJiKiNuFqAyQAuLFjAF6/p7If2r93/I3/HkyyytioMjh6fN0hi+AIAFLzjHh83SHEH0+x63gYIBERUZvQ3B5ITRnTJxxP3FK5kffz3/yFvReyrnpsbZ3JLGDhlpOorzmKeNvCLSft2tWcARIRETk9QRCQkFHVA6mRLtrN9X+3XYfhMSEoNwl4dN1BKTtFrbMvIbtO5qgmAUBKnhH7ErLtNiYGSERE5PSyi8qQb6zceDYy4OoDJLVahSVje6BnuC9yi8vx4Kf7kVtcdtXnbatS80qadVx6QcNBlLUxQCIiIqcnZnja+7pbbXm+zlWD1ZP7oL2vOxIyi/Do2oMoqzBb5dxtye/nMrBk25lmHRvkrbPxaKoxQCIiIqd3wQoF2vUJ9Nbik6l94aV1wd6EbMz55hi3GWqmi5lFeOizA5j08T4k5RjRWD9lFSpXs/WL8rfb+BggERGR07PGCraGXGfwxvsTekGjVuG/h5Lwwc7zVr+GMykwlmPRT6dw2zv/wy+n0uCiVmHaoEi8fV8PqFAZDNUkfr9gRFe79kNiHyQiInJ6CRm2C5AAYMi1gXjp7uvx4qbjeGvrGUQGeGJ4d/v27VE6s1nAfw4mYfHWM8gsrNzy5R/XBmL+XV1wTZA3AMBTq6nTB8kgUx8kBkhEROT0pAySFVawNWTSjRFIyCjCJ7sSMPvrIwj11eGGDn42u54jOXAxGwu3nMSxK3kAKgPVF+/qgluuC7LYq3RYtxDc1tXATtpERES2ZjYLSMiyTg+kprwwvAsuZRVh++l0PPz5AXz7z0EI9/ew6TWVLDm3BG/8dBqbjyYDALy1LvjX0M6YMjASbi71V/lo1CoM6BRgz2HWizVIRETk1JLzSlBWYYarRoX2vu42vZZGrcLy8Tega4gPMgvLMP2z/cg3tr39OEvKTFj2y1ncumQnNh9NhkoFjO8Xjl+fuRkP/6Njg8GRkih/hERERFdBnF7r4O8BF43tP/Y8tS74eGofBHlrcTatEDPWH0aFqW0s/xcEAVuOJmPokp1Y9ss5GMvN6Bfljy0zBmPRvd3Rzksr9xCbjQESERE5teoVbF52u2aI3h0fT+kLd1cNfjubgZe2nHD65f/Hr+Rh7Id78OSXh5GcZ0R7X3e8/0AvbHjkRnRrr5d7eC3GGiQiInJqF6pWsHW0YYF2fWLC9Hj3/p54dN1BrPszEVHtvDB9cJRdx2APGQWleHvrGXx98DIEAXB31eDxmzvhkX90tFpTTjkwQCIiIqdmyx5ITbn9egNeuLMLXv3hFF794SQi/D0Q2zXY7uOwhbIKMz7dnYDl2/9GYWnlNi6jeobiuTuiEaK3ba2XPcg+xfb+++8jMjISOp0O/fv3x759+xo9fuPGjYiOjoZOp0NMTAx+/PFHi/u/+eYb3H777QgICIBKpcKRI0fqnMNoNOKJJ55AQEAAvLy8MHr0aKSlpVnzaRERkULIGSABwPTBUXigfwcIAvCvrw7jeNVSd0clCAJ+OZmGuGW/4fUfT6OwtALdw/T47+MDsOz+G5wiOAJkDpA2bNiA2bNnY8GCBTh06BB69OiBuLg4pKen13v87t27MX78eEyfPh2HDx/GqFGjMGrUKBw/flw6pqioCIMHD8abb77Z4HWfeuopbNmyBRs3bsT//vc/JCcn495777X68yMiInmVVpiQlFMMwPZL/BuiUqmw8O7rcVPndiguM+Ghzw4gtZGd65XsXFoBJn+yDw99fgAJmUUI9Nbi7TE9sOmfg9A7wn7bgNiDSpCxaqx///7o27cv3nvvPQCA2WxGeHg4nnzySTz//PN1jh83bhyKiorw/fffS7fdeOON6NmzJ1auXGlx7MWLFxEVFYXDhw+jZ8+e0u15eXkIDAzE+vXrcd999wEATp8+jS5dumDPnj248cYbmzX2/Px86PV65OXlwcfHp6VPnYiI7ODv9ALELv0Nnm4aHF8YZ9GU0N7ySspx34rdOJdeiOtDffD1owPgqXWMSpfc4jIs++Uc1v55CSazADeNGtNvisITt1wDLwd5DqLmfn7LlkEqKyvDwYMHERsbWz0YtRqxsbHYs2dPvY/Zs2ePxfEAEBcX1+Dx9Tl48CDKy8stzhMdHY0OHTq06DxEVMlkFrDnfBa+O3IFe85nwWSW528upYxDadr66yIWaEcFesoaHAGA3t0Vn0ztiwBPN5xIzsfMr46grMKsmPenvp+VCpMZa/dcxM1v78Snuy/CZBYQd30wts3+B54bFu1wwVFLyPbMMjMzYTKZEBxsWawWHByM06dP1/uY1NTUeo9PTU1t9nVTU1Ph5uYGX1/fFp2ntLQUpaWl0vf5+fnNviaRs4o/nlJn36QQGfZNUso4lIavizxL/BsT7u+BVZP7YPzqP/HLqTTc8PLPKCozSffL9f7U97Pi7+kGnYsayVW3XRfsjfkjumLQNe3sOja5yF6k7SgWLVoEvV4vfYWHh8s9JCJZxR9PwePrDln8QgWA1DwjHl93CPHHU9rUOJSGr0sluQu069M7wg+TbowAAIvgCJDn/WnoZyW7qAzJeUZ4uGnwysjr8cO/BreZ4AiQMYPUrl07aDSaOqvH0tLSYDAY6n2MwWBo0fENnaOsrAy5ubkWWaSmzjNnzhzMnj1b+j4/P59BErVZJrOAhVtOor7JAPG25/57DNlFZVDbcFrDLAh4M/5Mg+NQAVi45SRu62qQZbNLuTT1/rSl1+VCpn32YGsJk1nAj8fqD4Ds+e8HaPzfkMhb54IH+kc4/c9KbbIFSG5ubujduze2b9+OUaNGAags0t6+fTtmzJhR72MGDBiA7du3Y9asWdJt27Ztw4ABA5p93d69e8PV1RXbt2/H6NGjAQBnzpxBYmJio+fRarXQah2nRTqRLe1LyK7z12ZteSXlmPvt8UaPsTUBQEqeEfsSshWx+aW9NPX+tKXXRYkZJEf59yNKyy9tEz8rtclaXTV79mxMmTIFffr0Qb9+/bBs2TIUFRVh2rRpAIDJkyejffv2WLRoEQBg5syZGDJkCJYsWYLhw4fjq6++woEDB7Bq1SrpnNnZ2UhMTERycuXOwWfOnAFQmTkyGAzQ6/WYPn06Zs+eDX9/f/j4+ODJJ5/EgAEDmr2CjaitSy9o3hLlbqE+MNiwJ0pqXgmOJzddD9jc8TqL5j5fZ39dCozlyCiorB2NVFCApJR/PwD/DTVG1gBp3LhxyMjIwPz585GamoqePXsiPj5eKsROTEyEWl1dJjVw4ECsX78e8+bNw9y5c9G5c2ds2rQJ3bp1k47ZvHmzFGABwP333w8AWLBgAV566SUAwDvvvAO1Wo3Ro0ejtLQUcXFx+OCDD+zwjImcQ5C3rlnHvTC8q03/6txzPgvjV//Z5HHNHa+zaO7zdfbX5WJmZf+jdl5u0Lu7yjyaakr59wPw31BjZO2D5MjYB4naMpNZwOA3dyA1z1hv7YIKgEGvwx/P3WrTugWljENp+LpU+u7IFcz86gj6Rvph42MD5R6OREnvj5LGYi+K74NERI5Lo1ZhwYiu9d4n/gpdMKKrzX+h1hxH7SvZcxxKo5T3R25KrD8ClPVzq6SxKA0DJCJqlWHdQvDBhF51fqka9DqsmNjLbn1chnULwYqJvWDQW04BBHpr7ToOpRFfF02tN8je74+clNYDqaaGfm7leH+UNBYlcd4WmERkc32j/KW0/NIxPRDi645+Uf52/2tzWLcQ3NbVgH0J2Xhi/UFkF5Vj+f034MY2tuqmttguwRbTJt46F/z+7C1w0bSNv42VmkES1fy5TS8wIshbJ8u/H6WNRSkYIBFRq13KqiyCbe/rjnt7h8k6Fo1ahQGdAhBt8MHu81m4klsi63iUICXPCLMAuGnUqDCbUWCsQHZxWZsouBUEAQlV24x0DFRmgARU/9wqgZLGogRt488IIrKJy9mVAVK4v22XIrdEuJ8HAOBy1Q7ubZn4/oT5uUvL3E+nFMg5JLvJLCxDQWkFVCqgg7+H3MMhB8QAiYhaTcwgRfgr5y90MVi7nM0Mkhgkhvl7oIuhcrXO6dS2sY+kOL3W3tcdOleNzKMhR8QAiYhaLbEqQ9EhQDl/oYf7M4MkEoPEcD93RBu8AbSdDFJCZiEA5dYfkfKxBomIWq16ik05AVJY1RRbUjYDJDFIDPf3QKfAypVcp1LbRoCkxD3YyLEwg0RErXYpu/JDKEJBAZI4xZaSb0RZhVnm0chLCmD9PKQM0t/pBSg3Of/rIhZoM4NErcUAiYhaxVhuQlp+5T5XSiqCDfTSQueqhiAAyW18JdvlnKopNn93hPm5w0vrgnKTgAtVwYMzk5b4ByqvBxI5BgZIRNQqSVXTN946F/h6KGefK5VKJU2zteU6JGO5SdqoNdzPAyqVqroOyckLtU1mAZeqsmecYqPWYoBERK0irmDr4F/54ask4X6V02xJOW03gyQ+dy9tdQAbHVIZIJ1y8kLt5NwSlFWY4aZRI9RXOS0oyLEwQCKiVpFWsCloek0krWRrw4Xa0hJ/P3cpgI1uI0v9xem1iACPNt0Jmq4OAyQiahUpg6SgJf6i6maRbTiDVM8Kwy4hbWOpv9K3GCHHwACJiFrlsqIzSGKzyLacQRJ7IFW/P9cGVwZIqflG5BSVyTIue6gu0GaARK3HAImIWkXJU2xSL6Q2XKRd3zYw3jpX6fvTTtwPiT2QyBoYIBFRi5nNghQgKWmbEZGYNcksLENxWYXMo5GH1CTSzzKAbQt1SNVdtLnEn1qPARIRtVhGYSlKK8zQqFUI8VXezvB6D1d46yo3CmirK9mkbUZqZfi6OPmWI6UVJuk9Zw0SXQ0GSETUYmL2KNRXB1eNMn+NSIXabbAOKd9YjryScgCVq9hqig5x7gxSYlYxBAHw1rqgnZeb3MMhB6bM32xEpGjiCjYlTq+J2nKhtvic/T3d4Km13HJTbBZ5Jq0AJrNg97HZ2oUaBdpK689FjoUBEhG1WKICN6mtrS0v9Zem1/zqNkmMCPCEzlUNY7kZl7Kcb8sRLvEna2GAREQtlphV3YhPqdpys0hx9V5YPQGsRq3CdcHiliPOV4fETWrJWhggEVGLKXmJv0iaYmuTGaT6V7CJpJVsKc5Xh8QMElkLAyQiarHEqikcRQdIYi+k7GIIgvPV2jRGahLpX/8+ZNKebE6YQarugcQl/nR1GCARUYsUlVYgs7Byl3glbjMiEptFFpRWSCu62opmZ5CcbCVbvrFc+tmMbKfcn01yDAyQiKhFxAaEvh6u8NG5yjyahrm7adDOSwugumi5LRAEQeoD1FARvbiS7XJ2CQqMzhM8XqzKHgV6a+Gt4J9NcgwMkIioRRKzlF9/JKquQ2o7hdqZhWUoKTdBparsU1UfP083GHwq7zub5jzTbKw/ImtigERELeIIBdqi8Da4J5v4XA0+OmhdNA0eJ9UhOVFH7QsZ3IONrIcBEhG1iEMFSFKzyLYzxSYVaDdQfyRyxjokZpDImhggEVGLOFSAJDWLbDsZJLFAO6yBFWyiLiHOtycbAySyJgZIRNQiUg2Sglewidpis0hxiq35GaQCp2iDIAiCFCB1DGSARFePARIRNZvJXL1CyhEySOJGrUk5JU4RBDSHtM1IE+9Px0BPuGpUKCytkN5TR5ZRWIrC0gqoVcreAoccBwMkImq21HwjykxmuGpUCNE3PoWjBKG+7lCrgNIKMzIKSuUejl1cljJIjb8/rho1rglyni1HxC1Gwvw8Gi1OJ2ouBkhE1Gzi9FqYnwc0auXvlO6qUUuBXFuoQzKZBSTnNi+DBABdDGIdkuMXarP+iKyNARIRNZvUodmBpjDEaba2sJItNd+IcpMAV40KwT7190CqSVzq7xQZJAZIZGUMkIio2S5lV34IRThQgNSWCrXF5xjq696sDJ9YqH3KCZb6X2CBNlkZAyQiajZH2KS2tra01L+pPdhqEzNIFzOLUFJmstm47IEZJLI2BkhE1GyJDjjF1paaRUpNIpvogSQK9NIiwNMNZgE4l+6402wms4BLWQyQyLoYIBFRsyVWfQhFOEAPJJE0xdYGMkhJ2dVF9M2hUqmq65AcuGHklZwSlJsEuLmoEeoAqyvJMTBAIqJmyTeWI6e4cud3h8ogVQULKXlGVJjMMo/GtqQl/i14f5yhDulCZiEAICrAE2oHWF1JjoEBEhE1i7jEP8DTDV5aF5lH03xB3lq4uahhMgtIyTPKPRybkppENtEDqaZog+NnkFh/RLbAAImImkUsAHaELUZqUqtVCPMV65Ccd5qttMKEtILKALAlGaQuIdWb1jpqt3EpQOIKNrIiBkhE1CyOtEltbWFtoA7pSk4JBAFwd9UgwNOt2Y+7JsgLahWQU1yOdAftNs4MEtkCAyQiapZLVQGSI/VAEoXX2JPNWSXVWMGmUjW/DkfnqkHHQC8AwCkH7ah9oWqbkY4MkMiKGCARUbM4YhdtUVtoFlm9B1vL3x+pDskBO2oby01IzqsMDplBImtigEREzeLIU2zVzSKdN4MkFWi34v2R6pAcMIN0KasYggD46Fzg34KpRaKmMEAioiZVmMy4UhVcRAQ43l/p1c0inT+DFNaCFWwiR84gJYhL/AO9WjS1SNQUBkhE1KSUPCMqzJWN+IK8tXIPp8XExonpBaUwljv2lhoNSbqKKdDoqgzS3+mFKKtwrF5R0h5snF4jK2OARERNkrYY8XN3yEZ8fh6u8HTTAHDeQm1pm5FW1CCF6nXw1rmgwizgfEahtYdmUwkZXMFGtiF7gPT+++8jMjISOp0O/fv3x759+xo9fuPGjYiOjoZOp0NMTAx+/PFHi/sFQcD8+fMREhICd3d3xMbG4ty5cxbHnD17FiNHjkS7du3g4+ODwYMH49dff7X6cyNyFpeqmkQ64vQaULmlhjNvOVJUWoHsojIAQFgz92GrSaVSoYuhuh+SI+ESf7IVWQOkDRs2YPbs2ViwYAEOHTqEHj16IC4uDunp6fUev3v3bowfPx7Tp0/H4cOHMWrUKIwaNQrHjx+Xjlm8eDGWL1+OlStXYu/evfD09ERcXByMxuoOunfddRcqKiqwY8cOHDx4ED169MBdd92F1NRUmz9nIkfkyAXaInGaLckJ65DEoE/v7gofnWurzuGoe7IxQCJbkTVAWrp0KR5++GFMmzYNXbt2xcqVK+Hh4YFPPvmk3uPfffddDBs2DM888wy6dOmCV155Bb169cJ7770HoDJ7tGzZMsybNw8jR45E9+7d8fnnnyM5ORmbNm0CAGRmZuLcuXN4/vnn0b17d3Tu3BlvvPEGiouLLQItIqqWmF35IeTIAZJUqO2EU2zVK9hav1Fr9Z5sjhMg5RWXI6sqc8YAiaxNtgCprKwMBw8eRGxsbPVg1GrExsZiz5499T5mz549FscDQFxcnHR8QkICUlNTLY7R6/Xo37+/dExAQACuu+46fP755ygqKkJFRQU+/PBDBAUFoXfv3g2Ot7S0FPn5+RZfRG2FM2SQpKX+zphBym59DyRRdQbJcX63JWRVBu7BPlp4OtD+gOQYZAuQMjMzYTKZEBwcbHF7cHBwg1NdqampjR4v/rexY1QqFX755RccPnwY3t7e0Ol0WLp0KeLj4+Hn59fgeBctWgS9Xi99hYeHt+wJEzkwcaNaR9uHrSZnrkGSmkReRQB7XXBlgJReUIqsQsfYckRa4s/sEdmA7EXa9iYIAp544gkEBQXh999/x759+zBq1CiMGDECKSkpDT5uzpw5yMvLk74uX75sx1ETySe3uAz5xgoAV5ehkFt1LyQnnmJrRQ8kkafWBRFVAfAZB5lmq17B5iXzSMgZyRYgtWvXDhqNBmlpaRa3p6WlwWAw1PsYg8HQ6PHifxs7ZseOHfj+++/x1VdfYdCgQejVqxc++OADuLu747PPPmtwvFqtFj4+PhZfRG2BOL0W5K2Fe9VSeUckBnd5JeXIN5bLPBrrShKbRF7lFKjYMNJR6pDYA4lsSbYAyc3NDb1798b27dul28xmM7Zv344BAwbU+5gBAwZYHA8A27Ztk46PioqCwWCwOCY/Px979+6VjikurvxFolZbPnW1Wg2z2bEapBHZgzPUHwGVGRJxKwpnqkMSBMEqNUhAdaG2o9QhcQUb2ZKsU2yzZ8/G6tWr8dlnn+HUqVN4/PHHUVRUhGnTpgEAJk+ejDlz5kjHz5w5E/Hx8ViyZAlOnz6Nl156CQcOHMCMGTMAVNYXzZo1C6+++io2b96MY8eOYfLkyQgNDcWoUaMAVAZZfn5+mDJlCo4ePYqzZ8/imWeeQUJCAoYPH27314BI6S45Qf2RSJyCcqZptpzichSVVXYHb802IzV1CXGcLUcEQagOkAIZIJH1yVr2P27cOGRkZGD+/PlITU1Fz549ER8fLxVZJyYmWmR6Bg4ciPXr12PevHmYO3cuOnfujE2bNqFbt27SMc8++yyKiorwyCOPIDc3F4MHD0Z8fDx0Oh2Ayqm9+Ph4vPDCC7j11ltRXl6O66+/Ht999x169Ohh3xeAyAFcdpIMElA5BXU0KU+aknIG4nMJ8tZC53p1U6BiBulsWgEqTGa4aJRbpppeUIriMhM0apVD18aRcsm+LnLGjBlSBqi2nTt31rltzJgxGDNmTIPnU6lUePnll/Hyyy83eEyfPn2wdevWFo+VqC2SMkhOECCJH6TOtN1IdQ+kq39/Ovh7wN1Vg5JyEy5mFeGaIO+rPqetXKgq0A73c4ebi3IDOXJc/KkiokaJNUgRzjDFJq1kc54MkrTE/yqn1wBArVbhOrFQW+EdtVl/RLbGAImIGlRWYUZKnvUyFHKTmkU60RSbVKBtpfenug5J2YXa1T2QuMSfbIMBEhE16EpuCcwC4O6qQaCXVu7hXLWwGkXagiDIPBrrELdOsVYdTvVKNgfJILFAm2yEARIRNajmEn+VSiXzaK5eez93qFRASblJ2sPL0Ymb74ZdxT5sNYm9kJS+ko09kMjWGCARUYMSrTx9IzetiwbB3pUrWp2hDslsFqSCc2tnkK7kliCvRJkNNStMZmn7G9Ygka0wQCKiBiVWbQbqDAXaIqlQ2wlWsqUXlKLMZIZGrUKIXmeVc+o9XBFadS6lbjmSlFOCCrMAnasaBh/rPG+i2hggEVGDnKWLdk1SobYTZJDEYvMQvc6qPYuiQ6rqkBRaqC3WH0UGeEKtdvypX1ImBkhE1CBn6oEkEvcrc4ZmkdbaYqS2aIUv9Zfqj1igTTbEAImI6lVzjy9n2GZE5EzbjVQ3ibROgbZI+RkkcYk/AySyHQZIRFSv7KIyFJWZoFIB7X2t+wEsJ7Hg3Bl6IVU3ibRuANulKoN0JrUAZrPy2iFUN4lkDySyHQZIRFSvS1XZI4OP7qr3+FISMUBKzi2BSYEf/i1h7SaRoqh2nnDTqFFcZlJkIJmQwS7aZHsMkIioXs60SW1NBh8dXDUqlJsEpOYb5R7OVZGW+Ft5is1Fo0bn4MrsjNLqkErKTEjOq3zf2AOJbIkBEhHVK9EJC7QBQKNWIdTX8fdkKzfV2AbGBrvZSx21FVaHdLGq9YSvhyv8PN1kHg05MwZIRFSvS060SW1tzrDUP7lqGxitixqB3tbfBkbak01hGSRuUkv2wgCJiOrlbF20axKnpJIcuFmkOPYwP3ebbAOj1AwSAySyFwZIRFQvZ61BAoAwP8dfyWarAm1RdFUG6VJ2MYpKK2xyjda4kME92Mg+GCARUR3GcpNUwBwR4HwfRGJQkeTAvZBstcRf1M5Li3ZeWggCcDZNOdNs1T2QuMSfbIsBEhHVkZRTAkEAvLQu8PNwlXs4VhcmNot06AySbVaw1STVISloTzZOsZG9MEAiojoSsys/hML9PWxS3yI3MeuSmm9EaYVJ5tG0jq0zSED1liOnU5RRh5RTVIac4nIAQGQ755v6JWVhgEREdYhL/COcsP4IANp5ucHdVQNBAJJzHbMXUnUGyZYBUmWh9imFZJASqpb4h+h18HBzkXk05OwYIBFRHYlVH77OtAdbTSqVqnqazQGX+peUmZBZWArAxhmkkOoMkiDI33X8IqfXyI4YIBFRHeIUmzOuYBM58p5sSVVj9ta5QG/DGrFrgrygUauQb6xASp78mTbWH5E9MUAiojoSnXiJvyhcyiA53ko2MagLs2H2CAC0Lhp0CqwMRpTQD+kCAySyIwZIRGRBEIS2ESA5cAZJqj/ys90KNpFUh6SAjtriJrUdAxkgke0xQCIiCxkFpTCWm6FWAe3t8AEsFzH7kuSANUi2bhJZU7RClvoLglBjio09kMj2GCARkQUxexTq6w5XjfP+ihD7B112wO1Gqpf42z6A7SJuOSLzUv+0/FKUlJvgoq4usCeyJef97UdErXIpy/mn14Dq7Et2UZmittJoDnss8ReJGaQLmUUwlsvXM+pCVQftDv4eTh24k3Lwp4yILIgZpAgnXeIv8tG5Qu9euQLM0eqQpAySHQIkg48OendXmMwC/k4vtPn1GsIVbGRvDJCIyII961vkJk2zOdBKtrzichQYKzNe9phqUqlU1R21ZaxDEgu0IxkgkZ0wQCIiC5fawAo2kdhk0ZGaRYrZo3ZebnbrJt0lRP46JGaQyN4YIBGRBWmKzd/5P4jELFmSAxVqJ9mpB1JNisggVQVIHRkgkZ0wQCIiSUmZCRkFlVtYtI0MkriSzYEySHYs0BZFixkkmZpFlpvMUuAexR5IZCcMkIhIIn4I6d1dbbqFhVKEOfAUmz2W+IuuDfaCSgVkFpZJAbQ9JeWUoMIswN1Vg2Bvnd2vT20TAyQikrSFDto1iUXaSTklitiMtTnkKKL3cHNBZIB8W44kVC3xj2znCbVaZffrU9vEAImIJG0tQBIzSIWlFcgtLpd5NM0jNrYMt2MNElCjDkmGLUcuZLD+iOyvRQHSvn37YDJVNwr7/vvvMWTIELRv3x59+vTB559/bvUBEpH9JGZVfhB1cPIeSCKdqwaB3loAjlGHJAiCVKQtZr/sRdqTTZYMElewkf21KEAaMGAAsrKyAABbtmzByJEjERkZiRdeeAE33HADpk+fjm+//dYmAyUi22trGSSgRqG2A/RCyiis3icv1NfOAVKIfBkkBkgkhxY10ag5R7948WI8++yzWLRokXRbVFQUFi9ejHvuucd6IyQiu2lLPZBE4f4eOJSY6xAZJDGIC9Hbf588cU+2v9MLUW4y2/X6UoDEFWxkR63+CT979izuu+8+i9tGjx6N06dPX/WgiMj+zGYBSVUfwG0qQHKglWzi9Fp7GTZrDfNzh6ebBmUmsxSw2ENxWQVS8owAWINE9tXiAOnkyZP466+/4O7uDrPZXOf+igrH2vSRiCqlFRhRZjLDRa1CiL7tLKWWthtxgGaR0go2OxdoA4BarcJ1VYXap+zYUftiZuVz9vNwha+Hm92uS9TiAGno0KHo2bMnEhMTsWvXLov7Dh8+jA4dOlhtcERkP5eyqrMTLm1ot3Qx2EhygAxSdZNI+2eQgJoNI+1Xh8T6I5JLi2qQEhISLL738vKy+L6srAzPPffc1Y+KiOyuLRZoA5bbjZjNgqL77FQ3iZTnPeoiLfW3XwZJ7IEU1c6riSOJrKtFAVJERESj90+ePPmqBkNE8rncRgOkEL0OGrUKZSYz0gtKYVDw9KIUIMn0HsmRQbog7sHGAm2ys7aTRyeiRolTbBFtpAeSyEWjlmqulLySrcJkRnJuZbGyXFNsYg1SSp4RucVldrkmp9hILlYNkGJjY9GxY0drnpKI7KStTrEBjrGSLSXPCJNZgJtGLdt+ZD46V7Sv6r9krywSAySSi1UDpHvuuQdTpkyx5imJyE4SZdjjSylq7smmVOLY2vu5y1on1SXEfnVIOUVl0hYw4l5wRPbSohqkpjzxxBPWPB0R2UmBsRzZRZVTJswgKZM4/RcmQw+kmqINPvjlVLpdMkhi/VGoXgd3N43Nr0dU01VlkEpLS1FaWmqtsRCRTMTl4/6ebvDWuco8GvsLk3ohKTdASlJIhk/ccuSUHQIkdtAmObU4QNq2bRvuvPNO+Pn5wcPDAx4eHvDz88Odd96JX375xRZjJCIbS8yu/CCS+8NXLtUZJOVOsYmNLOVa4i/qUrWS7WxqAUxmoYmjr071En8GSGR/LQqQPvvsM9x5553Q6/V455138P333+P777/HO++8A19fX9x5551Yu3Ztiwbw/vvvIzIyEjqdDv3798e+ffsaPX7jxo2Ijo6GTqdDTEwMfvzxR4v7BUHA/PnzERISAnd3d8TGxuLcuXN1zvPDDz+gf//+cHd3h5+fH0aNGtWicRM5E7H+KKKtBkhVzzslrwTlpro7BCiB1EVbphVsosgAT2hd1CgpN0k/N7ZSXaDNHkhkfy0KkF577TUsW7YMX375JaZOnYo77rgDd9xxB6ZOnYr169dj2bJlePnll5t9vg0bNmD27NlYsGABDh06hB49eiAuLg7p6en1Hr97926MHz8e06dPx+HDhzFq1CiMGjUKx48fl45ZvHgxli9fjpUrV2Lv3r3w9PREXFwcjEajdMx///tfTJo0CdOmTcPRo0exa9cuPPDAAy15KYicSltewQYAgV5auLmoYRaAlFxj0w+QgdxNIkWaGluO2LpQ+0JGVQ8kZpBIBi0KkBITExEbG9vg/UOHDkVSUlKzz7d06VI8/PDDmDZtGrp27YqVK1fCw8MDn3zySb3Hv/vuuxg2bBieeeYZdOnSBa+88gp69eqF9957D0Bl9mjZsmWYN28eRo4cie7du+Pzzz9HcnIyNm3aBKByr7iZM2firbfewmOPPYZrr70WXbt2xdixY5v/QhA5GbEHUoc21gNJpFarpOJnJdYhGctNSMuvrPdUwjRotMH2dUhms4CLWVziT/JpUYB0/fXX4+OPP27w/k8++QRdu3Zt1rnKyspw8OBBi4BLrVYjNjYWe/bsqfcxe/bsqROgxcXFSccnJCQgNTXV4hi9Xo/+/ftLxxw6dAhXrlyBWq3GDTfcgJCQENxxxx0WWaj6lJaWIj8/3+KLyFm01S7aNSl5JduV3Mr6I083Dfw85C+ijzZUddS2YQYpNd8IY3nl5slyr9yjtqlFy/yXLFmCu+66C/Hx8YiNjUVwcDAAIC0tDdu3b8eFCxfwww8/NOtcmZmZMJlM0jlEwcHBOH36dL2PSU1Nrff41NRU6X7xtoaOuXDhAgDgpZdewtKlSxEZGYklS5bg5ptvxtmzZ+Hv71/vtRctWoSFCxc267kRORKTWZB67LTpAEnBK9ku11jBplLJv1ecuJLNlkv9xfqjDgEebWrzZFKOFv3U3XzzzTh+/DjuuOMOHDx4EJ988gk++eQTHDx4EHfccQeOHTuGf/zjH7Yaq1WYzZUFmC+88AJGjx6N3r17Y82aNVCpVNi4cWODj5szZw7y8vKkr8uXL9tryEQ2lZxbgoqqDs0GH+XuQ2ZrSl7JJq5gU0omRcwgJWYXo7C0wibXkPZg4/QayaTFjSIjIyPx5ptvXvWF27VrB41Gg7S0NIvb09LSYDAY6n2MwWBo9Hjxv2lpaQgJCbE4pmfPngAg3V5zKlCr1aJjx45ITExscLxarRZarbaZz47IcYjZiTB/eTs0y02s7VFiBknsgRQmc4G2yN/TDcE+WqTll+JMagF6R/hZ/RoJGaw/InldVd7y7Nmz2Lt3LzIzM1v8WDc3N/Tu3Rvbt2+XbjObzdi+fTsGDBhQ72MGDBhgcTxQ2ZdJPD4qKgoGg8HimPz8fOzdu1c6pnfv3tBqtThz5ox0THl5OS5evIiIiIgWPw8iR3eJ9UcAlJ5BUkaTyJqkOqRU29QhVfdA4hJ/kkerthr55ptv8PTTT8NkMsFgMODcuXO45557sGLFCri5uTX7PLNnz8aUKVPQp08f9OvXD8uWLUNRURGmTZsGAJg8eTLat2+PRYsWAQBmzpyJIUOGYMmSJRg+fDi++uorHDhwAKtWrQIAqFQqzJo1C6+++io6d+6MqKgovPjiiwgNDZX6HPn4+OCxxx7DggULEB4ejoiICLz11lsAgDFjxrTm5SByaG29B5JIrEHKLCxFSZlJUVtbiEFbuEKm2IDKOqT/nc3A6RTb1CFxk1qSW4sDpA8++ABLlizBRx99hFtvvRUAUFRUhOnTp+OFF17AW2+9hZKSEri7N/0Pedy4ccjIyMD8+fORmpqKnj17Ij4+XiqyTkxMhFpdneQaOHAg1q9fj3nz5mHu3Lno3LkzNm3ahG7duknHPPvssygqKsIjjzyC3NxcDB48GPHx8dDpqmsr3nrrLbi4uGDSpEkoKSlB//79sWPHDvj5WT9NTKR0bXmT2pr07q7w1rqgoLQCSTnF6BzsLfeQJErMIHWxYQaprMIs1V115DYjJBOVIAjN7hV/8uRJ3HTTTdi/fz/c3NykgmcASE9Px2233Ybs7Gz06dMHH3/8sVT344zy8/Oh1+uRl5cHHx8fuYdD1Goj/v0Hjl3Jw6pJvXH79fXX/7UVd7z7O06l5OOTqX1wa3Rw0w+wgwJjOWJe+hkAcHxhHLy0Vt1jvNVOp+Zj2LLf4a11wV8v3W7V1XXnMwoxdMn/4OGmwYmFcYpYuUfOo7mf3y36l/bee+/hoYceQseOHREdHY0LFy6goqJyBYNKpUJoaCjS09MxceJELFy4EN9+++3VPQsisjlpii2Af6mH+7njVEq+ouqQxLH4ebgqJjgCgI7tvOCqUaGgtAJXckusWkBes0CbwRHJpUVF2jt37sSdd94JAJgxYwaGDRuGpKQk5OTk4P/+7/8wfPhwBAcHY8KECdi6dSvKy8ttMmgiso684nLklVT+O5V7jy8lEKewkhS0ki1JgdNrAODmokanwMoCamvXIbH+iJSgRQFSeno6goKCAFRuE/Lyyy8jNDQUer0er732GtasWYPc3FwEBQXBbDY3uKcaESmDmD0K9NbCw0052Qm5iEXQisog5YgF2soKkACgS4ht6pDYA4mUoEUBkp+fn7TXmouLi8VSeXG6zdXVFSUlJSgrK2NtDpHCtfVNamsTp4mU1AupZp8qpbHVnmzSEn8WaJOMWvQn46BBg7B9+3bcdttteOqppzB9+nT8+uuv8PT0xJdffolHHnkEnp6e+OGHH3DttdfC21s5q0CIqK5L2VXbOTBAAlCjWaSC9mOTptgUmEGKDrHNnmzVU2zsgUTyaVEG6bHHHsPq1auRkZGBxx9/HD/99BP0ej3MZjP+/e9/Y8WKFTCbzXj99dfx+OOP22rMRGQl3KTWkriVR76xQqrNkpvUA0mB71GXqgxSQmYRjOUmq5yzqLQCafmlAIAoLhwgGbUog3TjjTfigQcewIgRI/Ddd9/hpptuwk033STdbzKZ8NBDD0EQBDzxxBNWHywRWRen2Cx5al0Q4OmGrKIyXM4uhr69XtbxCIJQ3QNJQU0iRYHeWvh7uiG7qAzn0goRE3b1r5eYPQrwdIPew/Wqz0fUWi3eamT58uW46aab0L17dzz77LPYtGkTtm7dirfffhtdu3ZFRkYGfvzxR7i4sOCTSOkuZVUFSAEMkERhClrJll1UhuIyE1QqoL0CAySVSlWjDsk602xcwUZK0eIASaVS4a233sKvv/4KNzc3rFmzBsuXL8fly5fx4Ycf4vvvv4evr68NhkpE1lRuMiM5t3L6pq1vM1KTklayiSvYgr110LooZ+uTmqQ92ay01J8BEilFq9M8Xbt2xauvvmrNsRCRHSXnlsAsAFoXNQK9tXIPRzGkQm0FZJAuS9vAKC97JIoOqcwgWWupvxQgcQUbyaxFGSSz2Yw333wTgwYNQt++ffH888+jpET+v7KIqOWk6TV/D3YrrkFcLaaElWxikGbNLtXWJu7JdiolHy3YuapB7IFEStGiAOm1117D3Llz4eXlhfbt2+Pdd99lMTaRg6reYkS5H75yELM14vSWnKQVbAqsPxJ1DvaCWgXkFJcjo6D0qs4lCAISMqp6IHGJP8msRQHS559/jg8++ABbt27Fpk2bsGXLFnzxxRcWm9YSkWNIzFbmFhZyEzNISTnFVsmIXA2xUDxMwe+RzlUj1QtdbcPI7KIy5BsroFIxcCf5tShASkxMlPZiA4DY2FioVCokJydbfWBEZFuJVVNsLNC2FOrrDpUKMJabkVF4dRmRqyXVICl4ig2wXsNIsf4oVO8Onasyi9Kp7WhRgFRRUQGdTmdxm6urKzelJXJAUg8k/qVuwc1FjRCfyt9zcq5kM5kFXMkVm0Qqd4oNqG4YefoqM0hS/RELtEkBWrSKTRAETJ06FVpt9YoXo9GIxx57DJ6e1T/Q33zzjfVGSERWJwgCm0Q2IszfA8l5RiTlFKN3hJ8sY0jLN6LcJMBFrUKIXtkBUnSNQu2rwSX+pCQtCpCmTJlS57aJEydabTBEZB85xeUoLK0AoOwVUnIJ9/PAvoRsWVeyidcO9XWHRq3sVYbiUv/zGYUoqzDDzaXFLfYAAAkZDJBIOVoUIK1Zs8ZW4yAiOxKzRwYfHWs96iFOaSXJuJJNvLbSp9cAoL2vO7y1LigorcCFzEIpo9RSzCCRkrQuzK+HIAj46aefcN9991nrlERkI5eyKj+IOL1WPzGrJmezyOo92JT/HqlUquqGka3sqG02C0jIEnsgcYk/ye+qA6SEhAS8+OKL6NChA+655x4YjUZrjIuIbOgyC7QbpYTtRqQeSA4SxEp1SK3sqJ2cV4KyCjNcNSpF7jtHbU+rthopLS3Ff/7zH3z88cf4448/YDKZ8Pbbb2P69Onw8WldapWI7IcF2o0Tg5Lk3BKYzIIsNUDVXbQdI1i42gySOL0WEeCp+JorahtalEE6ePAg/vnPf8JgMGDZsmUYNWoULl++DLVajbi4OAZHRA6i5jYjVFewjw6uGhUqzAJS8uTJIiU5WCNPadPaVmaQWH9EStOiAKl///7QarX4888/sX//fvzrX/9CcHCwrcZGRDbCKbbGadQqtPeVb5qtrMKMlPzKcgVHqEECgOuqeiGl5Zciu6isxY+/kME92EhZWhQgDR06FB9//DFefvllxMfHy96Gn4harrTCJH34MoPUMDFzI0ehdnJuCQQBcHfVoJ2Xm92v3xpeWhfp56k1WSRmkEhpWhQgbd26FSdOnMB1112Hxx9/HCEhIZg5cyYAcDdwIgeRlFP54evppkGAp2N8+MpBXMmWJEMvpJr1R470uzXa0Po6JAZIpDQtXsUWHh6O+fPnIyEhAWvXrkVGRgZcXFwwcuRIzJ07FwcPHrTFOInISmpuUutIH772JvYfuixDLyRHW8EmkvZka2EGqbTCJG3MG8VtRkghrmqZ/2233Yb169cjOTkZ//rXv/DTTz+hX79+1hobEdlAIgu0m0Ws/ZGjm3Z1DyTHWMEmau2ebJezi2EWKqfpAr20TT+AyA5atcwfqNyD7a+//kJ6ejrMZjM6dOiAhQsX4vz589YcHxFZmZhBimCBdqPkrEESgzJH2wZGzCCdSS1oUXuECzW2GGFWk5SiVQFSfHw8Jk+ejMzMzDr3qVQqPPXUU1c9MCKyDfZAah4xe5OWXwpjucmuW7JcdqBtRmrq4O8Bd1cNSspNuJhVhE6BzeuIzfojUqJWTbE9+eSTGDNmDFJSUmA2my2+TCaTtcdIRFYkTrE5Wn2Lvfl7usHDrTIoupJr3zqkJAfNIGnUKlzbikJtBkikRK0KkNLS0jB79mz2QCJyMIIg1Jhi44dRY1QqlSx1SEWlFciq6iPkiEFsdR1S8wu1L1QFSB1ZoE0K0qoA6b777sPOnTutPBQisrWMwlKUlJugVkFqhEgNk2MlW1LVtXx0LtC7u9rtutYiLvU/1YIM0kVmkEiBWlWD9N5772HMmDH4/fffERMTA1dXy3/E//rXv6wyOCKyLjETEqJ3h5vLVe9V7fSkXkh2LNQWr+WI2SOg5Uv9C0srkF5QCgCIZIBECtKqAOnLL7/Ezz//DJ1Oh507d1qsOlCpVAyQiBSKBdotI24Um2TH7UbEINZRthipTcwgJeWUIN9YDh9d41kwMXvUzkvb5LFE9tSqAOmFF17AwoUL8fzzz0Ot5l+hRI6Cm9S2jBxL/R11BZvI18MNIXodUvKMOJtagD6R/o0eL9UfMXtECtOq6KasrAzjxo1jcETkYBK5SW2LyFGkfTnbsafYgBp1SM1oGJmQwfojUqZWRThTpkzBhg0brD0WIrKxy5xiaxExi5NTXI7C0gq7XFPKIDnoFBtQow4ppek6pITMQgDcYoSUp1VTbCaTCYsXL8bWrVvRvXv3OkXaS5cutcrgiMi6OMXWMt46V/h6uCK3uByXs4vRpeqD31YEQZB6IDnqFBtQY9Pa5mSQuIKNFKpVAdKxY8dwww03AACOHz9ucR/bxBMpU0mZSVotxG1Gmi/czwO5xXl2CZDySspRUJWpcrQmkTV1qbHliNksQN3AliOCILAGiRSrVQHSr7/+au1xEJGNicvHvR20v45cwv3dcexKnl16IV2uWi0X6K2169Ym1hbVzhNuGjUKSytwJbekwXqqrKIyFBgroFKxLo6Uh1XWRG1Ezek1Znqbz56F2uJqOXEfOEflqlHjmqDKfdhONVKHJE6vhfm5Q+viuAEhOScGSERtRPUWI/xLvSXC/O3XLNIZVrCJokOarkOqXsHWvE1tieyJARJRG5HoRB++9iRmcy7boVlkdQbJ8d+jLoamO2qz/oiUjAESURshZZD8+WHUEjWbRQqCYNNriUFYmINPsQE1MkiN7MkmLfFngEQKxACJqI3gNiOtI27qW1xmQnZRmU2vddnB92GrKboqg5SQVYSSMlO9x3CJPykZAySiNsBsFhggtZLOVYNgHy0A2HQlm9ksIMkJmkSKAr21aOflBkEAzqbVzSKZzAIuVi0cYIBESsQAiagNSC8oRVmFGRq1CqG+OrmH43DssZIto7DyPVKrgBAneY+iG6lDSs4tQVmFGW4uaoT6Ov6UIjkfBkhEbYCYPWrv6w4XDf/Zt5Q9Nq0Vg68QvTtcneQ9kvZkq6cOSZxeiwzwgKaBRpJEclLEv8L3338fkZGR0Ol06N+/P/bt29fo8Rs3bkR0dDR0Oh1iYmLw448/WtwvCALmz5+PkJAQuLu7IzY2FufOnav3XKWlpejZsydUKhWOHDliradEpCiXsio/jDi91jpi0XSSDafYpOk1B95ipDZpT7Z6MkisPyKlkz1A2rBhA2bPno0FCxbg0KFD6NGjB+Li4pCenl7v8bt378b48eMxffp0HD58GKNGjcKoUaMstjxZvHgxli9fjpUrV2Lv3r3w9PREXFwcjEZjnfM9++yzCA0NtdnzI1ICaZNa9kBqFXtMsUk9kJyg/khUc0+22isAqwMk9kAiZZI9QFq6dCkefvhhTJs2DV27dsXKlSvh4eGBTz75pN7j3333XQwbNgzPPPMMunTpgldeeQW9evXCe++9B6Aye7Rs2TLMmzcPI0eORPfu3fH5558jOTkZmzZtsjjXTz/9hJ9//hlvv/22rZ8mkawusUD7qoT52z6D5Ewr2ETXBHlBo1Yht7gcafmlFvexBxIpnawBUllZGQ4ePIjY2FjpNrVajdjYWOzZs6fex+zZs8fieACIi4uTjk9ISEBqaqrFMXq9Hv3797c4Z1paGh5++GGsXbsWHh7O8wuJqD5cwXZ1xKzOlZwSmM226YUk9kBypik2natGCoBO1Zpmk3ogBTJAImWSNUDKzMyEyWRCcHCwxe3BwcFITU2t9zGpqamNHi/+t7FjBEHA1KlT8dhjj6FPnz7NGmtpaSny8/MtvogcxWUGSFclRK+DRq1CmcmMtIK6U/XW4ExdtGuS6pBqFGqXVpikbBxrkEipZJ9ik8O///1vFBQUYM6cOc1+zKJFi6DX66Wv8PBwG46QyHoKSyuQWVjZ4JA1SK3jolFL7RFsseVIhcmMlLzKwMuZptiAmnVI1X9UJmYVQxAAb50LAjzd5BoaUaNkDZDatWsHjUaDtLQ0i9vT0tJgMBjqfYzBYGj0ePG/jR2zY8cO7NmzB1qtFi4uLrjmmmsAAH369MGUKVPqve6cOXOQl5cnfV2+fLmFz5ZIHmL2yM/DFT46V5lH47hsWaidkmeEySzAzUWNQC+t1c8vpy71bDlSs/5IpeISf1ImWQMkNzc39O7dG9u3b5duM5vN2L59OwYMGFDvYwYMGGBxPABs27ZNOj4qKgoGg8HimPz8fOzdu1c6Zvny5Th69CiOHDmCI0eOSG0CNmzYgNdee63e62q1Wvj4+Fh8ETkC1h9ZhxQg2aAXkhh0hfm5Q+1kPYHEZpHnMwpRWlG55QiX+JMjcJF7ALNnz8aUKVPQp08f9OvXD8uWLUNRURGmTZsGAJg8eTLat2+PRYsWAQBmzpyJIUOGYMmSJRg+fDi++uorHDhwAKtWrQIAqFQqzJo1C6+++io6d+6MqKgovPjiiwgNDcWoUaMAAB06dLAYg5dX5TLTTp06ISwszE7PnMg+ErOcb3WUHMTiaVtMsTlr/RFQWb/lo3NBvrEC59OL0DXUBwkZXOJPyid7gDRu3DhkZGRg/vz5SE1NRc+ePREfHy8VWScmJkKtrk50DRw4EOvXr8e8efMwd+5cdO7cGZs2bUK3bt2kY5599lkUFRXhkUceQW5uLgYPHoz4+HjodM7Rvp+oJcQMUgTrj66KLbtpO+MKNpFKpUJ0iA/2JWTjdGp+ZYAkZpC4go0UTPYACQBmzJiBGTNm1Hvfzp0769w2ZswYjBkzpsHzqVQqvPzyy3j55Zebdf3IyMg6TcyInAV7IFlHWFV2J8kGNUjOnEECgC4G76oAqbIOiT2QyBG0yVVsRG2J1KGZAdJVEbM7KflGlFWYrXru6hok53yPxKX+p1LykW8sR2ZhZdPISAZIpGAMkIicmMksIClHnGLjh9HVCPTSQueqhiBU7kRvTZedcB+2mmpuOXKxKnsU5K2Fl1YRkxhE9WKAROTEUvJKUG4S4KpRweDDGryroVKppAyPNeuQjOUmZBRUZlScdYrt2mBvqFRARkEp9l/MAcAVbKR8DJCInFhijQ1QNU62fFwO4X7WX8kmZvi8tC7w9XDOPlWeWhdEVE3xxh9PAQB0ZIE2KRwDJCInxvoj67JFBkkMtsL83J26aaLYD+nAJWaQyDEwQCJyYpeyuILNmsQaIXEfMWsQM0jOHsRGV3XUFhcMswcSKR0DJCInxh5I1mWL7UakAm0nrT8SXRvkbfE9g3ZSOgZIRE6MU2zWJb6OSVadYhPfI+dcwQZU1h0t2HzC4rYpn+yT6pGIlIgBEpETY5NI6xKzPJmFZSguq7DKOZ29SWT88RQ8vu4QMqp6H4nS8o14fN0hBkmkWAyQiJxUXkk5covLATBAsha9hyu8dZW9e6xVh1S9zYjzvUcms4CFW06ivn0KxNsWbjkJk5k7GZDyMEAiclLi1E07Lzd4siGf1VizDinfWI68ksogNszP+abY9iVkIyXP2OD9AoCUPCP2JWTbb1BEzcQAichJJbL+yCbEWiFrBEjiOQI8nTOITS9oODhqzXFE9sQAichJSSvYGCBZlZRBssIUm9QDyUnfoyDv5nVvb+5xRPbEAInISbEHkm2IGTlrZJCkHkhOOL0GAP2i/BGi16Gh9pcqACF6HfpF+dtzWETNwgCJyEmJH+AduEmtVUlTbFbJIDn3NKhGrcKCEV0BoE6QJH6/YERXboNDisQAichJJXKJv02IU2xJ2cUQhKtbfdUWmkQO6xaCFRN7waC3nEYz6HVYMbEXhnULkWlkRI1zvqpAIkK5yYwruZUfvgyQrEvcj62gtAJ5JeXw9XBr9bnEDJIzrmCraVi3ENzW1YB9CdlILzAiyLtyWo2ZI1IyBkhETigl1wiTWYDWRY0gb63cw3Eq7m4atPPSIrOwFJezS1odIAmCIPVSctYptpo0ahUGdAqQexhEzcYpNiIndCm7CEDlB6+af6VbXXUdUusLtTMLy1BSboJKBYT6chUXkdIwQCJyQqw/sq0wKzSLFIMrg48OWheNVcZFRNbDAInICTFAsi1xWf7VbDeS1AYKtIkcGQMkIieUyB5INiX1QrqKKTapQNvfuQu0iRwVAyQiJ8QMkm1ZYz+26iaRfI+IlIgBEpGTEQRByiBFBPDD1xbEIu2knJJW90IStxlpCyvYiBwRAyQiJ5NbXI6C0goA/PC1lVBfd6hVQGmFGRkFpa06x2Un32aEyNExQCJyMuL0WrCPFjpXro6yBVeNGiH61i/1N5kFJOcyg0SkZAyQiJwM64/sQ+x+LU6VtURqvhHlJgGuGhWCfdgDiUiJGCAROZlEJ98AVSmklWytKNQWH9Pe153bbRApFAMkIicjFWj7e8o8EucmrWRrxRTbZQaxRIrHAInIyYjbjHQIYPGvLUnbjbRiiu1yVZPIMC7xJ1IsBkhETkb8wGYNkm1dTbPIJCmDxCCWSKkYIBE5kbIKM5LzxACJU2y2JE6xpeQZUWEyt+ixl9kkkkjxGCAROZGknGIIAuDhpkE7Lze5h+PUgry1cHNRw2QWkJJnbNFjxSxfGHsgESkWAyQiJ1Jzib9KxdVRtqRWqxDmK9YhNX+arbTChLSCyoCKRdpEysUAiciJcHWUfYW1og7pSk4JBAFwd9UgwJNZPiKlYoBE5EQuZbFJpD21plmkuIIt3N+dWT4iBWOARORExCk2blJrH2KRdVILMkhJLNAmcggMkIicCLto25fUCymnBRmkbO7BRuQIGCAROQlBELgPm51J3bRbUKQt1itxBRuRsjFAInISWUVlKC4zQaXih6+9iFmg9IJSGMtNzXpMErN8RA6BARKRkxALtEN8dNC6aGQeTdvg5+EKT7fK1zqpmdNsUpE2a5CIFI0BEpGT4BJ/+1OpVC3acqSotALZRWUAuM0IkdIxQCJyElzBJg9xw9mkZtQhiUGUr4crvHWuNh0XEV0dBkhEToI9kOTRkpVs0go2Tq8RKR4DJCInIU6xdQjgJrX21JKVbNXToJxeI1I6BkhEToJL/OXRkhqky2wSSeQwGCAROQFjuQmp+ZUboDJAsi9piq0Z242Ix4TxPSJSPAZIRE5A3L7CW+sCPw8W/9qTmA3KKylHvrG80WOrtxnhFBuR0jFAInICYoF2uL8HN0C1M0+tC/w93QA0XockCIJ0fxin2IgUTxEB0vvvv4/IyEjodDr0798f+/bta/T4jRs3Ijo6GjqdDjExMfjxxx8t7hcEAfPnz0dISAjc3d0RGxuLc+fOSfdfvHgR06dPR1RUFNzd3dGpUycsWLAAZWVlNnl+RLbG+iN5iRmhxqbZcorLUVRW2W2bnc6JlE/2AGnDhg2YPXs2FixYgEOHDqFHjx6Ii4tDenp6vcfv3r0b48ePx/Tp03H48GGMGjUKo0aNwvHjx6VjFi9ejOXLl2PlypXYu3cvPD09ERcXB6Oxskbj9OnTMJvN+PDDD3HixAm88847WLlyJebOnWuX50xkbeyBJC+pF1Ijhdpi9ijIWwudKzudEymd7AHS0qVL8fDDD2PatGno2rUrVq5cCQ8PD3zyySf1Hv/uu+9i2LBheOaZZ9ClSxe88sor6NWrF9577z0AldmjZcuWYd68eRg5ciS6d++Ozz//HMnJydi0aRMAYNiwYVizZg1uv/12dOzYEXfffTeefvppfPPNN/Z62kRWlZjFLtpyCpMKtRsJkHL4HhE5ElkDpLKyMhw8eBCxsbHSbWq1GrGxsdizZ0+9j9mzZ4/F8QAQFxcnHZ+QkIDU1FSLY/R6Pfr379/gOQEgLy8P/v7+Dd5fWlqK/Px8iy8ipeAUm7zCpQxSw1NsSdIebJxeI3IEsgZImZmZMJlMCA4Otrg9ODgYqamp9T4mNTW10ePF/7bknH///Tf+/e9/49FHH21wrIsWLYJer5e+wsPDG39yRHYiCAKn2GTWnF5I3CuPyLHIPsUmtytXrmDYsGEYM2YMHn744QaPmzNnDvLy8qSvy5cv23GURA1LLyhFaYUZGrUKob7MTsihZpG2IAj1HnM5h9uMEDkSWQOkdu3aQaPRIC0tzeL2tLQ0GAyGeh9jMBgaPV78b3POmZycjFtuuQUDBw7EqlWrGh2rVquFj4+PxReREojZo1BfHVw1bf5vHlm093OHSgWUlJuQVVT/alhxM9swbjNC5BBk/W3q5uaG3r17Y/v27dJtZrMZ27dvx4ABA+p9zIABAyyOB4Bt27ZJx0dFRcFgMFgck5+fj71791qc88qVK7j55pvRu3dvrFmzBmo1P1jIMXGTWvlpXTQI9tYBqL9Q22wWatQg8X0icgQucg9g9uzZmDJlCvr06YN+/fph2bJlKCoqwrRp0wAAkydPRvv27bFo0SIAwMyZMzFkyBAsWbIEw4cPx1dffYUDBw5IGSCVSoVZs2bh1VdfRefOnREVFYUXX3wRoaGhGDVqFIDq4CgiIgJvv/02MjIypPE0lLkiUioWaCtDuL87UvONuJxTghs6+Fncl15QijJT5TRoiF4n0wiJqCVkD5DGjRuHjIwMzJ8/H6mpqejZsyfi4+OlIuvExESL7M7AgQOxfv16zJs3D3PnzkXnzp2xadMmdOvWTTrm2WefRVFRER555BHk5uZi8ODBiI+Ph05X+Ytp27Zt+Pvvv/H3338jLCzMYjwN1Q8QKdVlKUDylHkkbVu4nwf2X8ypN4MkFm+H+urgwmlQIoegEhgRtEp+fj70ej3y8vJYj0SyuveDXTiUmIv3H+iF4d1D5B5Om7V021ks334O4/uFY9G93S3u++ZQEmZ/fRQDOwVg/cM3yjRCIgKa//nNP2WIHFxi1fYWnGKTV2PbjYi3sf6IyHEwQCJyYMVlFcgsLAUAdGAPJFk11gupuos2V7AROQoGSEQOTCzQ1ru7Qu/uKvNo2jYxQErOLYHJbFm5wCaRRI6HARKRAxP3YGMHbfkZfHRw1ahQbhKQmm+0uE9c4h/GKTYih8EAiciBJTIzoRg1O5nXXMlWbjIjJY/7sBE5GgZIRA6MPZCUJcyvboCUnFsCswBoXdQI9NbKNTQiaiEGSEQOTNqklgGSIoir1MR914DqFWxhfu5QqVSyjIuIWo4BEpEDS+Q2I4oiTnUm1cggVa9g43tE5EgYIBE5KFPN/b344asI4hRbUo0MUpIYILFAm8ihMEAiclBp+UaUmcxwqVEcTPKqrxeS1CSSPZCIHAoDJCIHdalqei3Mzx0aNWtblEDMEqXmG1FaYQJQY4qNGSQih8IAicgBmcwCdp5JBwB461zqNCYkebTzcoO7qwaCACTnVvZCqs4gMUAiciQMkIgcTPzxFAx+cwc+/O0CAODYlXwMfnMH4o+nyDwyUqlUFkv9S8pM0lYwzCARORYGSEQOJP54Ch5fdwgpeZadmlPzjHh83SEGSQpQsw5JLND21rlA78GtYIgcCQMkUjyTWcCe81n47sgV7Dmf1Wank0xmAQu3nER9z168beGWk2329VGKcCmDVML6IyIH5iL3AIgaE388BQu3nLTImITodVgwoiuGdQuRcWT2VVJmwn8OJdXJHNUkAEjJM2JfQjYGdAqw3+DIQs0MUoheV3UbV7ARORoGSKRY4nRS7XyIOJ20YmIvpwuSBEFASp4Rp1Lyq74KcColHwlZRRCamRhKL2g4iCLbEzekTcouRohPVYDEDBKRw2GARIrU1HSSCpXTSbd1NdhtibvJLGBfQjbSC4wI8tahX5T/VV3bWG7C3+mFOCkFQ/k4nVqA3OLyeo/30bkg31jR5HmDvHWtHhNdPTFbdDmnBAY9u2gTOSoGSApi7Q9gR7YvIbtZ00kPfbYfMWG+CPbRIthbh2AfHYJ9tAjw0lr1tbvaqb70AqOUDRK/zmcU1VsvpFGr0CnQE11CfGp8eSPAU4vBb+5Aap6x3sBRBcCgr/y5IfmIwVB2URnOpBZU3cYpNiJHwwBJIVhrY6m500S/nsnAr2cy6tyuVgGB3loE++gQ5F0ZNInBU5CPriqY0sLPww3qJgKplkz1lZvMOJ9RaDE9diolH5mFZfWeW+/uii4h3lIg1DXEB9cEeUHnqqn3+AUjuuLxdYegAizGo6pxf1sNqpXCR+cKvbsr8krKcTGLRdpEjooBkgK0xVqbpjR3mmhM7zC4uaiRll+K9AIj0vKNyCgohVkA0vJLkZZfCiCvwce7alQI8tYhSMpAVQVQVcFUOy8tFmw+0ejKsWf/8xe2nkjFmdRC/J1eiDKTuc6xKhUQFeApZYPEgChEr2vRDu/DuoVgxcRedYJpQxsOppUozM8deSXVU6Xt/ZhBInI0DJBkpsRaGyX480JWo/eL00lvjO5e53UxmQVkFZZWBUhGpBUYKwOo/MoASgymMgvLUG4ScCW3BFdyS+q/UDPkGyvw7eFk6XtvrQuiawRBXUJ8cF2wN9zd6s8KtdSwbiG4rauB07EKFu7ngRPJ+QAqu2t7uPFXLZGj4b9amTW31qatLN0WBAHvbDuL5Tv+lm5r6XSSRq1CkI8OQT46xEDf4LXKKszILCy1CJrE/0/LNyI9vxSXc4pRXGZqctzDuhlwzw3t0TXEB2F+7i3KCrWGRq1qEz8Pjqq9X3UGVO/uCpNZYABL5GAYIMmsubU2bWHptiAIWLz1DFbsPA8AeOHOLgj3d7fZdJKbixqhvu4I9W14+mPP+SyMX/1nk+eaMiCSAQsBqJwy33ggSfr+fEYRBr+5g1OgRA6GAZLMmltr4+xLtwVBwKKfTmNV1f5i8+/qigcHRwGArNNJ/aL8EaLXceUYNQvrCYmcB7cakZn4AdzUx31OUaldxiMHQRDw8vcnpeDolZHXS8ERUD2dNLJnewzoFGDXqQqNWoUFI7oCQJ33iCvHqCZuBUPkXBggyaw5H8AA8MSXh/HxHwl2G5e9mM0CFmw+gTW7LgIAXr8nBpMGRMo6ptrElWMGvWUWz6DXMSNAkpbUExKR8nGKTQEaW7o9b3gX7LmQhXV/JuKV708iKacY84Y7R8bCbBYw77vjWL83ESoV8Oa93TG2b7jcw6oXV45RU1hPSORcGCApRGMfwHfGhCDMzwNv/HQaa3ZdREquEcvu79lgM0FHYDYLmPPNMWw4cBkqFfD2fT0wuneY3MNqFFeOUWNYT0jkXDjFpiAN1dqoVCo8NqQT3r2/J9w0asSfSMUDq/9EdlH93ZmVzmQW8PR/jmLDgctQq4Bl43oqPjgiakpT9YQqVHbHZ0E/kWNggORARvZsj7XT+8FH54JDibm494NduJhZJPewWqTCZMbsr4/gm0NXoFGrsHz8DRjZs73cwyK6aizoJ3IuDJAcTP+OAfjmnwPR3tcdF7OKce+K3TiUmCP3sJql3GTGrA1H8N2RZLioVXhv/A24q3uo3MMishoW9BM5D5UgCFxz2gr5+fnQ6/XIy8uDj4+P3a+fXmDEg5/ux/Er+dC6qPHu/TdgWDeD3cfRXGUVZsz86jB+Op4KV40K7z/QC7dfr9zxEl0Nk1lgQT+RQjX385sBUivJHSABQFFpBWasP4Rfz2RApapsrjhtUFTTD7Szsgoznlh/CNtOpsFNo8aKib0wtEuw3MMiIqI2qLmf35xic2CeWhesntwHD/TvAEGobEL3yvcnYVZQI7rSChMeX3ewMjhyUWPV5N4MjoiISPEYIDk4F40ar43qhmeHXQcA+PiPBDyx/hCM5U1vsGprxnITHvn8ILafTofWRY2Pp/TBzdcFyT0sIiKiJjFAcgIqlQr/vPkavHt/T7hqVPjpeComfLRX1jYAJWUmPPz5AfzvbAbcXTVYM7UvbuocKNt4iIiIWoIBkhMZ2bM9Pn+wP7x1Ljh4KQejV+zGpSz7twEoLqvAg5/ux+/nMuHhpsGn0/pi4DXt7D4OIiKi1mKA5GQGdArAfx+vbAOQkFmEez/YjcN2bANQVFqBqWv2Y8+FLHhpXfD5g/3QvyO7TxMRkWNhgOSErg32xrf/HIjrQ32QVVSG8av/xM8nUm1+3QJjOaZ8sg/7ErLhrXXB59P7oU8kuwYTEZHjYYDkpIJ8dPj60QG4+bpAGMvNeHTdQXy2+6LNrpdvLMfkT/bhwKUc+OhcsO6h/ujVwc9m1yMiIrIlBkhOzFPrgo8m98H9fcMhCMCCzSfw2g/WbwOQV1yOSR/txeHEXPh6uGL9wzeiR7ivVa9BRERkTwyQnJyLRo1F98bgmbjKNgCrf0/Ak18etlobgJyiMkz4+E8cTcqDn4cr1j90I7q111vl3ERERHJhgNQGqFQqPHHLNXhnXA+4alT44VgKJn60FzlX2QYgq7AUD3y0F8ev5CPA0w1fPnIjuobK01WciIjImhggtSH33BCGzx7sB2+dCw5UtQFIzCpu1bkyC0vxwOq9OJWSj3ZeWnz1yI2INjA4IiIi58AAqY0Z2Kkd/vPYQITqdbiQWYR7PtiFI5dzW3SO9AIjxq/6E2fSChDkXRkcdQ72ts2AiYiIZMAAqQ26zuCNb58YhK4hlW0A7l+1B9tOpjXrsWn5Rty/6k+cSy9EiF6HDY8OwDVBXjYeMRERkX0xQGqjgn10+PqxAfjHtVVtANYewOd7Ljb6mJS8Eoz7cA8uZBShva87NjwyAFHtPO0zYCIiIjtigNSGeWld8PGUPhjXJxxmAZj/3Qks+vEUzGYBJrOAPeez8N2RK9hzPguXsoow7sM/cTGrGGF+7vjqkRvRIcBD7qdARERkE4oIkN5//31ERkZCp9Ohf//+2LdvX6PHb9y4EdHR0dDpdIiJicGPP/5ocb8gCJg/fz5CQkLg7u6O2NhYnDt3zuKY7OxsTJgwAT4+PvD19cX06dNRWFho9eemdK4aNd4YHYP/u+1aAMCHv13AmA93Y9Ab2zF+9Z+Y+dURjF/9J255eycSs4sREeCBDY8OQLg/gyMiInJesgdIGzZswOzZs7FgwQIcOnQIPXr0QFxcHNLT0+s9fvfu3Rg/fjymT5+Ow4cPY9SoURg1ahSOHz8uHbN48WIsX74cK1euxN69e+Hp6Ym4uDgYjUbpmAkTJuDEiRPYtm0bvv/+e/z222945JFHbP58lUilUuHJoZ2xZEwPqFXAwUu5SM0vtThG7C352JCOaO/rLsMoiYiI7EclCIJ12yq3UP/+/dG3b1+89957AACz2Yzw8HA8+eSTeP755+scP27cOBQVFeH777+XbrvxxhvRs2dPrFy5EoIgIDQ0FP/3f/+Hp59+GgCQl5eH4OBgfPrpp7j//vtx6tQpdO3aFfv370efPn0AAPHx8bjzzjuRlJSE0NDQJsedn58PvV6PvLw8+Pg4x/J2k1lA39d+QXYj/ZFC9Dr88dyt0KhVdhwZERGRdTT381vWDFJZWRkOHjyI2NhY6Ta1Wo3Y2Fjs2bOn3sfs2bPH4ngAiIuLk45PSEhAamqqxTF6vR79+/eXjtmzZw98fX2l4AgAYmNjoVarsXfv3nqvW1paivz8fIsvZ7MvIbvR4AgAUvKM2JeQbacRERERyUPWACkzMxMmkwnBwcEWtwcHByM1tf7d51NTUxs9XvxvU8cEBQVZ3O/i4gJ/f/8Gr7to0SLo9XrpKzw8vJnP0nGkFxibPqgFxxERETkq2WuQHMWcOXOQl5cnfV2+fFnuIVldkLfOqscRERE5KlkDpHbt2kGj0SAtzbJJYVpaGgwGQ72PMRgMjR4v/repY2oXgVdUVCA7O7vB62q1Wvj4+Fh8OZt+Uf4I0evQUHWRCpU1SP2i/O05LCIiIruTNUByc3ND7969sX37duk2s9mM7du3Y8CAAfU+ZsCAARbHA8C2bduk46OiomAwGCyOyc/Px969e6VjBgwYgNzcXBw8eFA6ZseOHTCbzejfv7/Vnp+j0ahVWDCiKwDUCZLE7xeM6MoCbSIicnqyT7HNnj0bq1evxmeffYZTp07h8ccfR1FREaZNmwYAmDx5MubMmSMdP3PmTMTHx2PJkiU4ffo0XnrpJRw4cAAzZswAULlkfdasWXj11VexefNmHDt2DJMnT0ZoaChGjRoFAOjSpQuGDRuGhx9+GPv27cOuXbswY8YM3H///c1awebMhnULwYqJvWDQW06jGfQ6rJjYC8O6hcg0MiIiIvtxkXsA48aNQ0ZGBubPn4/U1FT07NkT8fHxUpF1YmIi1OrqOG7gwIFYv3495s2bh7lz56Jz587YtGkTunXrJh3z7LPPoqioCI888ghyc3MxePBgxMfHQ6er/tD/4osvMGPGDAwdOhRqtRqjR4/G8uXL7ffEFWxYtxDc1tWAfQnZSC8wIsi7clqNmSMiImorZO+D5KicsQ8SERGRs3OIPkhERERESsQAiYiIiKgWBkhEREREtTBAIiIiIqqFARIRERFRLQyQiIiIiGphgERERERUCwMkIiIioloYIBERERHVIvtWI45KbECen58v80iIiIioucTP7aY2EmGA1EoFBQUAgPDwcJlHQkRERC1VUFAAvV7f4P3ci62VzGYzkpOT4e3tDZXKOTdxzc/PR3h4OC5fvsz95qrwNakfX5e6+JrUxdekfnxd6rLlayIIAgoKChAaGgq1uuFKI2aQWkmtViMsLEzuYdiFj48P/9HWwtekfnxd6uJrUhdfk/rxdanLVq9JY5kjEYu0iYiIiGphgERERERUCwMkapBWq8WCBQug1WrlHopi8DWpH1+Xuvia1MXXpH58XepSwmvCIm0iIiKiWphBIiIiIqqFARIRERFRLQyQiIiIiGphgERERERUCwMkatQbb7wBlUqFWbNmyT0U2V25cgUTJ05EQEAA3N3dERMTgwMHDsg9LNmYTCa8+OKLiIqKgru7Ozp16oRXXnmlyf2NnM1vv/2GESNGIDQ0FCqVCps2bbK4XxAEzJ8/HyEhIXB3d0dsbCzOnTsnz2DtpLHXpLy8HM899xxiYmLg6emJ0NBQTJ48GcnJyfIN2E6a+lmp6bHHHoNKpcKyZcvsNj45NOc1OXXqFO6++27o9Xp4enqib9++SExMtPnYGCBRg/bv348PP/wQ3bt3l3sossvJycGgQYPg6uqKn376CSdPnsSSJUvg5+cn99Bk8+abb2LFihV47733cOrUKbz55ptYvHgx/v3vf8s9NLsqKipCjx498P7779d7/+LFi7F8+XKsXLkSe/fuhaenJ+Li4mA0Gu08Uvtp7DUpLi7GoUOH8OKLL+LQoUP45ptvcObMGdx9990yjNS+mvpZEX377bf4888/ERoaaqeRyaep1+T8+fMYPHgwoqOjsXPnTvz111948cUXodPpbD84gageBQUFQufOnYVt27YJQ4YMEWbOnCn3kGT13HPPCYMHD5Z7GIoyfPhw4cEHH7S47d577xUmTJgg04jkB0D49ttvpe/NZrNgMBiEt956S7otNzdX0Gq1wpdffinDCO2v9mtSn3379gkAhEuXLtlnUArQ0OuSlJQktG/fXjh+/LgQEREhvPPOO3Yfm1zqe03GjRsnTJw4UZbxMINE9XriiScwfPhwxMbGyj0URdi8eTP69OmDMWPGICgoCDfccANWr14t97BkNXDgQGzfvh1nz54FABw9ehR//PEH7rjjDplHphwJCQlITU21+Hek1+vRv39/7NmzR8aRKUteXh5UKhV8fX3lHoqszGYzJk2ahGeeeQbXX3+93MORndlsxg8//IBrr70WcXFxCAoKQv/+/RudmrQmBkhUx1dffYVDhw5h0aJFcg9FMS5cuIAVK1agc+fO2Lp1Kx5//HH861//wmeffSb30GTz/PPP4/7770d0dDRcXV1xww03YNasWZgwYYLcQ1OM1NRUAEBwcLDF7cHBwdJ9bZ3RaMRzzz2H8ePHt/mNWt988024uLjgX//6l9xDUYT09HQUFhbijTfewLBhw/Dzzz/jnnvuwb333ov//e9/Nr++i82vQA7l8uXLmDlzJrZt22afOV4HYTab0adPH7z++usAgBtuuAHHjx/HypUrMWXKFJlHJ4+vv/4aX3zxBdavX4/rr78eR44cwaxZsxAaGtpmXxNqmfLycowdOxaCIGDFihVyD0dWBw8exLvvvotDhw5BpVLJPRxFMJvNAICRI0fiqaeeAgD07NkTu3fvxsqVKzFkyBCbXp8ZJLJw8OBBpKeno1evXnBxcYGLiwv+97//Yfny5XBxcYHJZJJ7iLIICQlB165dLW7r0qWLXVZSKNUzzzwjZZFiYmIwadIkPPXUU8w81mAwGAAAaWlpFrenpaVJ97VVYnB06dIlbNu2rc1nj37//Xekp6ejQ4cO0u/eS5cu4f/+7/8QGRkp9/Bk0a5dO7i4uMj2u5cZJLIwdOhQHDt2zOK2adOmITo6Gs899xw0Go1MI5PXoEGDcObMGYvbzp49i4iICJlGJL/i4mKo1ZZ/Y2k0GumvPgKioqJgMBiwfft29OzZEwCQn5+PvXv34vHHH5d3cDISg6Nz587h119/RUBAgNxDkt2kSZPq1HzGxcVh0qRJmDZtmkyjkpebmxv69u0r2+9eBkhkwdvbG926dbO4zdPTEwEBAXVub0ueeuopDBw4EK+//jrGjh2Lffv2YdWqVVi1apXcQ5PNiBEj8Nprr6FDhw64/vrrcfjwYSxduhQPPvig3EOzq8LCQvz999/S9wkJCThy5Aj8/f3RoUMHzJo1C6+++io6d+6MqKgovPjiiwgNDcWoUaPkG7SNNfaahISE4L777sOhQ4fw/fffw2QySfVY/v7+cHNzk2vYNtfUz0rtQNHV1RUGgwHXXXedvYdqN029Js888wzGjRuHf/zjH7jlllsQHx+PLVu2YOfOnbYfnCxr58ihcJl/pS1btgjdunUTtFqtEB0dLaxatUruIckqPz9fmDlzptChQwdBp9MJHTt2FF544QWhtLRU7qHZ1a+//ioAqPM1ZcoUQRAql/q/+OKLQnBwsKDVaoWhQ4cKZ86ckXfQNtbYa5KQkFDvfQCEX3/9Ve6h21RTPyu1tYVl/s15TT7++GPhmmuuEXQ6ndCjRw9h06ZNdhmbShDaWNtbIiIioiawSJuIiIioFgZIRERERLUwQCIiIiKqhQESERERUS0MkIiIiIhqYYBEREREVAsDJCIiIqJaGCARkcO5+eabMWvWrGYff/HiRahUKhw5csRmYyIi58IAiYiaLSMjA25ubigqKkJ5eTk8PT0b3TQyMjISKpWqwa+pU6e2ahzffPMNXnnllWYfHx4ejpSUFJtvl8NAjMh5cC82Imq2PXv2oEePHvD09MTevXul/ZIasn//fphMJgDA7t27MXr0aJw5c0baud3d3d3i+PLycri6ujY5Dn9//xaNW6PRwGAwtOgxRNS2MYNERM22e/duDBo0CADwxx9/SP/fkMDAQBgMBhgMBimoCQoKgsFggNFohK+vLzZs2IAhQ4ZAp9Phiy++QFZWFsaPH4/27dvDw8MDMTEx+PLLLy3OW3uKLTIyEq+//joefPBBeHt7o0OHDhYbCdfO7OzcuRMqlQrbt29Hnz594OHhgYEDB9bZNfzVV19FUFAQvL298dBDD+H5559Hz549m/16idfZunUrbrjhBri7u+PWW29Feno6fvrpJ3Tp0gU+Pj544IEHUFxcLD0uPj4egwcPhq+vLwICAnDXXXfh/PnzFufevXs3evbsCZ1Ohz59+mDTpk11slfHjx/HHXfcAS8vLwQHB2PSpEnIzMyU7v/Pf/6DmJgYuLu7IyAgALGxsSgqKmr28yNyanbZ8Y2IHNalS5cEvV4v6PV6wdXVVdDpdIJerxfc3NwErVYr6PV64fHHH2/yPOKmlDk5OYIgCNKmpZGRkcJ///tf4cKFC0JycrKQlJQkvPXWW8Lhw4eF8+fPC8uXLxc0Go2wd+9e6Vy1N1COiIgQ/P39hffff184d+6csGjRIkGtVgunT5+2uNbhw4ctxtK/f39h586dwokTJ4SbbrpJGDhwoHTOdevWCTqdTvjkk0+EM2fOCAsXLhR8fHyEHj16NPgcG7rOjTfeKPzxxx/CoUOHhGuuuUYYMmSIcPvttwuHDh0SfvvtNyEgIEB44403pPP85z//Ef773/8K586dEw4fPiyMGDFCiImJEUwmkyAIgpCXlyf4+/sLEydOFE6cOCH8+OOPwrXXXmtx7ZycHCEwMFCYM2eOcOrUKeHQoUPCbbfdJtxyyy2CIAhCcnKy4OLiIixdulRISEgQ/vrrL+H9998XCgoKmnwvidoCBkhE1Kjy8nIhISFBOHr0qODq6iocPXpU+PvvvwUvLy/hf//7n5CQkCBkZGQ0eZ6GAqRly5Y1+djhw4cL//d//yd9X1+ANHHiROl7s9ksBAUFCStWrLC4Vu3A5ZdffpEe88MPPwgAhJKSEkEQBKF///7CE088YTGOQYMGtSpAqnmdRYsWCQCE8+fPS7c9+uijQlxcXIPnzcjIEAAIx44dEwRBEFasWCEEBARIYxUEQVi9erXFtV955RXh9ttvtzjP5cuXBQDCmTNnhIMHDwoAhIsXLzZ4XaK2jFNsRNQoFxcXREZG4vTp0+jbty+6d++O1NRUBAcH4x//+AciIyPRrl27Vp+/T58+Ft+bTCa88soriImJgb+/P7y8vLB169ZGi8EBoHv37tL/q1QqGAwGpKenN/sxISEhACA95syZM+jXr5/F8bW/b66a1wkODoaHhwc6duxocVvNsZ47dw7jx49Hx44d4ePjg8jISACQXoMzZ86ge/fu0Ol0DY7t6NGj+PXXX+Hl5SV9RUdHAwDOnz+PHj16YOjQoYiJicGYMWOwevVq5OTktOr5ETkjFmkTUaOuv/56XLp0CeXl5TCbzfDy8kJFRQUqKirg5eWFiIgInDhxotXn9/T0tPj+rbfewrvvvotly5YhJiYGnp6emDVrFsrKyho9T+3ibpVKBbPZ3OzHqFQqAGjyMa1R+zpNjXXEiBGIiIjA6tWrERoaCrPZjG7dujX5GtRUWFiIESNG4M0336xzX0hICDQaDbZt24bdu3fj559/xr///W+88MIL2Lt3L6KiolrxLImcCzNIRNSoH3/8EUeOHIHBYMC6detw5MgRdOvWDcuWLcORI0fw448/WvV6u3btwsiRIzFx4kT06NEDHTt2xNmzZ616jea47rrrsH//fovban9vC1lZWThz5gzmzZuHoUOHokuXLnUyO9dddx2OHTuG0tLSBsfWq1cvnDhxApGRkbjmmmssvsSgVKVSYdCgQVi4cCEOHz4MNzc3fPvttzZ/jkSOgAESETUqIiICXl5eSEtLw8iRIxEeHo4TJ05g9OjRuOaaaxAREWHV63Xu3FnKbJw6dQqPPvoo0tLSrHqN5njyySfx8ccf47PPPsO5c+fw6quv4q+//pIyTbbi5+eHgIAArFq1Cn///Td27NiB2bNnWxzzwAMPwGw245FHHsGpU6ewdetWvP322wCqM2FPPPEEsrOzMX78eOzfvx/nz5/H1q1bMW3aNJhMJuzduxevv/46Dhw4gMTERHzzzTfIyMhAly5dbPr8iBwFAyQiatLOnTvRt29f6HQ67Nu3D2FhYVLNjrXNmzcPvXr1QlxcHG6++WYYDAaMGjXKJtdqzIQJEzBnzhw8/fTT6NWrFxISEjB16lSLuh9bUKvV+Oqrr3Dw4EF069YNTz31FN566y2LY3x8fLBlyxYcOXIEPXv2xAsvvID58+cDgDS+0NBQ7Nq1CyaTCbfffjtiYmIwa9Ys+Pr6Qq1Ww8fHB7/99hvuvPNOXHvttZg3bx6WLFmCO+64w6bPj8hRqARBEOQeBBGRI7jttttgMBiwdu1auYdSxxdffIFp06YhLy+vTgNOImo5FmkTEdWjuLgYK1euRFxcHDQaDb788kv88ssv2LZtm9xDAwB8/vnn6NixI9q3b4+jR4/iueeew9ixYxkcEVkJAyQionqoVCr8+OOPeO2112A0GnHdddfhv//9L2JjY+UeGgAgNTUV8+fPR2pqKkJCQjBmzBi89tprcg+LyGlwio2IiIioFhZpExEREdXCAImIiIioFgZIRERERLUwQCIiIiKqhQESERERUS0MkIiIiIhqYYBEREREVAsDJCIiIqJaGCARERER1fL/PZeBvBWGSbEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ---- Settings ----\n", + "TRAIN_JSON = \"result.json\"\n", + "TRAIN_DIR = \"images\"\n", + "TEST_DIR = \"Test\"\n", + "CSV_FILE = \"labels.csv\"\n", + "GT_JSON = \"ground_truth.json\"\n", + "DEVICE = torch.device(\"cpu\")\n", + "RESIZE = Resize((320, 320))\n", + "BATCH_SIZE = 1\n", + "INITIAL = 3\n", + "VAL_SIZE = 2\n", + "POOL_BATCH = 1\n", + "ROUNDS = 14\n", + "\n", + "# sampling strategy: 'random' or 'uncertainty'\n", + "SAMPLING = 'uncertainty'\n", + "\n", + "class DaisyDataset(Dataset):\n", + " def __init__(self, csv, img_dir):\n", + " self.df = pd.read_csv(csv)\n", + " self.img_dir = img_dir\n", + " self.imgs = self.df['image_path'].unique().tolist()\n", + " def __len__(self):\n", + " return len(self.imgs)\n", + " def __getitem__(self, idx):\n", + " fn = self.imgs[idx]\n", + " img = Image.open(os.path.join(self.img_dir, fn)).convert(\"RGB\")\n", + " img = RESIZE(img)\n", + " recs = self.df[self.df['image_path'] == fn]\n", + " boxes = recs[['xmin','ymin','xmax','ymax']].values.astype(np.float32)\n", + " labels = recs['label'].values.astype(np.int64)\n", + " target = {'boxes': torch.from_numpy(boxes), 'labels': torch.from_numpy(labels), 'image_id': torch.tensor([idx])}\n", + " return F.to_tensor(img), target\n", + "\n", + "def collate_fn(batch):\n", + " return tuple(zip(*batch))\n", + "\n", + "def parse_coco(json_file, img_dir):\n", + " coco = COCO(json_file)\n", + " records = []\n", + " for ann in coco.loadAnns(coco.getAnnIds()):\n", + " img = coco.loadImgs(ann['image_id'])[0]\n", + " fn = os.path.basename(img['file_name'])\n", + " x,y,w,h = ann['bbox']\n", + " records.append({'image_path': fn, 'xmin': x, 'ymin': y, 'xmax': x+w, 'ymax': y+h, 'label_orig': ann['category_id']})\n", + " df = pd.DataFrame(records)\n", + " df.to_csv(\"labels_raw.csv\", index=False)\n", + " return df\n", + "\n", + "def build_coco_gt(df, out_json):\n", + " images, anns, cats = [], [], []\n", + " uniq = df['image_path'].unique().tolist()\n", + " for i, fn in enumerate(uniq): images.append({'id': i, 'file_name': fn})\n", + " aid = 1\n", + " for _, r in df.iterrows():\n", + " x1,y1,x2,y2 = map(int, [r['xmin'], r['ymin'], r['xmax'], r['ymax']])\n", + " w, h = x2-x1, y2-y1\n", + " anns.append({'id': aid, 'image_id': uniq.index(r['image_path']), 'category_id': int(r['label']), 'bbox': [x1,y1,w,h], 'area': float(w*h), 'iscrowd': 0})\n", + " aid += 1\n", + " for cid in sorted(df['label'].unique()): cats.append({'id': int(cid), 'name': str(cid)})\n", + " coco_dict = {'info':{}, 'licenses':[], 'images':images, 'annotations':anns, 'categories':cats}\n", + " with open(out_json, 'w') as f: json.dump(coco_dict, f)\n", + " return COCO(out_json)\n", + "\n", + "def train_epoch(model, opt, loader, device):\n", + " model.train()\n", + " for imgs, tgts in loader:\n", + " imgs = [i.to(device) for i in imgs]\n", + " tgts = [{k:v.to(device) for k,v in t.items()} for t in tgts]\n", + " losses = model(imgs, tgts)\n", + " loss = sum(losses.values())\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + "@torch.no_grad()\n", + "def evaluate_map(model, loader, device, coco_gt):\n", + " model.eval()\n", + " preds = []\n", + " for imgs, tgts in loader:\n", + " imgs = [i.to(device) for i in imgs]\n", + " outs = model(imgs)\n", + " for tgt, out in zip(tgts, outs):\n", + " img_id = int(tgt['image_id'].item())\n", + " for box, score, label in zip(out['boxes'].cpu(), out['scores'].cpu(), out['labels'].cpu()):\n", + " x1,y1,x2,y2 = box.tolist()\n", + " preds.append({'image_id': img_id, 'category_id': int(label), 'bbox': [x1,y1,x2-x1,y2-y1], 'score': float(score)})\n", + " if not preds:\n", + " print(\"→ No detections, mAP@0.5=0.0\")\n", + " return 0.0\n", + " with open(\"preds.json\",\"w\") as f: json.dump(preds, f)\n", + " coco_dt = coco_gt.loadRes(\"preds.json\")\n", + " ev = COCOeval(coco_gt, coco_dt, iouType=\"bbox\")\n", + " ev.params.imgIds = sorted(coco_gt.getImgIds())\n", + " ev.evaluate(); ev.accumulate(); ev.summarize()\n", + " return ev.stats[1]\n", + "\n", + "@torch.no_grad()\n", + "def select_uncertain(pool_idx, dataset, model, device, k):\n", + " uncertainties = []\n", + " for idx in pool_idx:\n", + " img, _ = dataset[idx]\n", + " out = model([img.to(device)])[0]\n", + " max_score = out['scores'].max().item() if len(out['scores'])>0 else 0.0\n", + " uncertainties.append((1 - max_score, idx))\n", + " uncertainties.sort(reverse=True, key=lambda x: x[0])\n", + " return [idx for _, idx in uncertainties[:k]]\n", + "\n", + "def main():\n", + " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", + " mapping = {orig: i+1 for i, orig in enumerate(df['label_orig'].unique())}\n", + " print(\"Label map:\", mapping)\n", + " df['label'] = df['label_orig'].map(mapping)\n", + " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", + " coco_gt = build_coco_gt(df, GT_JSON)\n", + "\n", + " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", + " n_classes = df['label'].nunique() + 1\n", + " idxs = list(range(len(dataset)))\n", + " random.shuffle(idxs)\n", + " train_idx = idxs[:INITIAL]\n", + " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", + " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", + "\n", + " model = fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, pretrained_backbone=True, num_classes=n_classes).to(DEVICE)\n", + " opt = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", + "\n", + " perf, sizes = [], []\n", + " for r in range(ROUNDS):\n", + " print(f\"Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", + " tr_loader = DataLoader(Subset(dataset, train_idx), batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", + " val_loader= DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn)\n", + "\n", + " train_epoch(model, opt, tr_loader, DEVICE)\n", + " mAP50 = evaluate_map(model, val_loader, DEVICE, coco_gt)\n", + " print(f\" mAP@0.5 = {mAP50:.4f}\")\n", + " perf.append(mAP50); sizes.append(len(train_idx))\n", + "\n", + " if not pool_idx:\n", + " break\n", + "\n", + " if SAMPLING == 'random':\n", + " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", + " else:\n", + " add = select_uncertain(pool_idx, dataset, model, DEVICE, min(POOL_BATCH, len(pool_idx)))\n", + " train_idx += add\n", + " pool_idx = [i for i in pool_idx if i not in add]\n", + "\n", + " plt.plot(sizes, perf, marker='o', label=SAMPLING)\n", + " plt.xlabel(\"# Training Images\"); plt.ylabel(\"mAP@0.5\")\n", + " plt.title(\"Daisy Active Learning\"); plt.legend(); plt.show()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "eddfc36b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "loading annotations into memory...\n", + "Done (t=0.00s)\n", + "creating index...\n", + "index created!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random → Round 1/48, train=3\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + "Random → Round 2/48, train=4\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + "Random → Round 3/48, train=5\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 4/48, train=6\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 5/48, train=7\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Random → Round 6/48, train=8\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Random → Round 7/48, train=9\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 8/48, train=10\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 9/48, train=11\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 10/48, train=12\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 11/48, train=13\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + "Random → Round 12/48, train=14\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 13/48, train=15\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + "Random → Round 14/48, train=16\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 15/48, train=17\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Random → Round 16/48, train=18\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 17/48, train=19\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 18/48, train=20\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 19/48, train=21\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 20/48, train=22\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 21/48, train=23\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 22/48, train=24\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.01s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 23/48, train=25\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + "Random → Round 24/48, train=26\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 25/48, train=27\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 26/48, train=28\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 27/48, train=29\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Random → Round 28/48, train=30\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 29/48, train=31\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 30/48, train=32\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Random → Round 31/48, train=33\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 32/48, train=34\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 33/48, train=35\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 34/48, train=36\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 35/48, train=37\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 36/48, train=38\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 37/48, train=39\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 38/48, train=40\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 39/48, train=41\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 40/48, train=42\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 41/48, train=43\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 42/48, train=44\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 43/48, train=45\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 44/48, train=46\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 45/48, train=47\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Random → Round 46/48, train=48\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 47/48, train=49\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Random → Round 48/48, train=50\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", + " warnings.warn(\n", + "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uncertainty → Round 1/48, train=3\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 2/48, train=4\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 3/48, train=5\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 4/48, train=6\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 5/48, train=7\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 6/48, train=8\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + "Uncertainty → Round 7/48, train=9\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + "Uncertainty → Round 8/48, train=10\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 9/48, train=11\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 10/48, train=12\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 11/48, train=13\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 12/48, train=14\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.03s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 13/48, train=15\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 14/48, train=16\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 15/48, train=17\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + "Uncertainty → Round 16/48, train=18\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 17/48, train=19\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 18/48, train=20\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 19/48, train=21\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + "Uncertainty → Round 20/48, train=22\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 21/48, train=23\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 22/48, train=24\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Uncertainty → Round 23/48, train=25\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 24/48, train=26\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", + "Uncertainty → Round 25/48, train=27\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Uncertainty → Round 26/48, train=28\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 27/48, train=29\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 28/48, train=30\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 29/48, train=31\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 30/48, train=32\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Uncertainty → Round 31/48, train=33\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 32/48, train=34\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 33/48, train=35\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + "Uncertainty → Round 34/48, train=36\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 35/48, train=37\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 36/48, train=38\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 37/48, train=39\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.03s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Uncertainty → Round 38/48, train=40\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 39/48, train=41\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Uncertainty → Round 40/48, train=42\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + "Uncertainty → Round 41/48, train=43\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 42/48, train=44\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 43/48, train=45\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.03s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 44/48, train=46\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", + "Uncertainty → Round 45/48, train=47\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", + "Uncertainty → Round 46/48, train=48\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + "Uncertainty → Round 47/48, train=49\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", + "Uncertainty → Round 48/48, train=50\n", + "Loading and preparing results...\n", + "DONE (t=0.00s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=0.02s).\n", + "Accumulating evaluation results...\n", + "DONE (t=0.01s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import random\n", + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader, Subset\n", + "from torchvision.models.detection import fasterrcnn_mobilenet_v3_large_320_fpn\n", + "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n", + "from torchvision.transforms import functional as F, Resize\n", + "from pycocotools.coco import COCO\n", + "from pycocotools.cocoeval import COCOeval\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# ---- Settings ----\n", + "TRAIN_JSON = \"result.json\"\n", + "TRAIN_DIR = \"images\"\n", + "CSV_FILE = \"labels.csv\"\n", + "GT_JSON = \"ground_truth.json\"\n", + "DEVICE = torch.device(\"cpu\")\n", + "RESIZE = Resize((320, 320))\n", + "BATCH_SIZE = 1\n", + "INITIAL = 3\n", + "VAL_SIZE = 2\n", + "POOL_BATCH = 1\n", + "ROUNDS = 48\n", + "EPOCHS_PER_ROUND = 5\n", + "SAMPLINGS = ['random', 'uncertainty']\n", + "\n", + "class DaisyDataset(Dataset):\n", + " def __init__(self, csv, img_dir):\n", + " self.df = pd.read_csv(csv)\n", + " self.img_dir = img_dir\n", + " self.imgs = self.df['image_path'].unique().tolist()\n", + "\n", + " def __len__(self):\n", + " return len(self.imgs)\n", + "\n", + " def __getitem__(self, idx):\n", + " fn = self.imgs[idx]\n", + " img = Image.open(os.path.join(self.img_dir, fn)).convert(\"RGB\")\n", + " img = RESIZE(img)\n", + " recs = self.df[self.df['image_path'] == fn]\n", + " boxes = recs[['xmin','ymin','xmax','ymax']].values.astype(np.float32)\n", + " labels = recs['label'].values.astype(np.int64)\n", + " target = {\n", + " 'boxes': torch.from_numpy(boxes),\n", + " 'labels': torch.from_numpy(labels),\n", + " 'image_id': torch.tensor([idx])\n", + " }\n", + " return F.to_tensor(img), target\n", + "\n", + "def collate_fn(batch):\n", + " return tuple(zip(*batch))\n", + "\n", + "def parse_coco(json_file, img_dir):\n", + " coco = COCO(json_file)\n", + " records = []\n", + " for ann in coco.loadAnns(coco.getAnnIds()):\n", + " img = coco.loadImgs(ann['image_id'])[0]\n", + " fn = os.path.basename(img['file_name'])\n", + " x,y,w,h = ann['bbox']\n", + " records.append({\n", + " 'image_path': fn,\n", + " 'xmin': x,\n", + " 'ymin': y,\n", + " 'xmax': x+w,\n", + " 'ymax': y+h,\n", + " 'label_orig': ann['category_id']\n", + " })\n", + " df = pd.DataFrame(records)\n", + " return df\n", + "\n", + "def build_coco_gt(df, out_json):\n", + " images, anns, cats = [], [], []\n", + " uniq = df['image_path'].unique().tolist()\n", + " for i, fn in enumerate(uniq):\n", + " images.append({'id': i, 'file_name': fn})\n", + " aid = 1\n", + " for _, r in df.iterrows():\n", + " x1,y1,x2,y2 = map(int, [r['xmin'], r['ymin'], r['xmax'], r['ymax']])\n", + " w, h = x2-x1, y2-y1\n", + " anns.append({\n", + " 'id': aid,\n", + " 'image_id': uniq.index(r['image_path']),\n", + " 'category_id': int(r['label']),\n", + " 'bbox': [x1, y1, w, h],\n", + " 'area': float(w*h),\n", + " 'iscrowd': 0\n", + " })\n", + " aid += 1\n", + " for cid in sorted(df['label'].unique()):\n", + " cats.append({'id': int(cid), 'name': str(cid)})\n", + " coco_dict = {\n", + " 'info': {}, 'licenses': [], \n", + " 'images': images, \n", + " 'annotations': anns, \n", + " 'categories': cats\n", + " }\n", + " with open(out_json, 'w') as f:\n", + " json.dump(coco_dict, f)\n", + " return COCO(out_json)\n", + "\n", + "def train_epoch(model, opt, loader, device):\n", + " model.train()\n", + " for imgs, tgts in loader:\n", + " imgs = [i.to(device) for i in imgs]\n", + " tgts = [{k: v.to(device) for k, v in t.items()} for t in tgts]\n", + " losses = model(imgs, tgts)\n", + " loss = sum(losses.values())\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + "@torch.no_grad()\n", + "def evaluate_map(model, loader, device, coco_gt):\n", + " model.eval()\n", + " preds = []\n", + " for imgs, tgts in loader:\n", + " imgs = [i.to(device) for i in imgs]\n", + " outs = model(imgs)\n", + " for tgt, out in zip(tgts, outs):\n", + " img_id = int(tgt['image_id'].item())\n", + " for box, score, label in zip(\n", + " out['boxes'].cpu(),\n", + " out['scores'].cpu(),\n", + " out['labels'].cpu()\n", + " ):\n", + " x1,y1,x2,y2 = box.tolist()\n", + " preds.append({\n", + " 'image_id': img_id,\n", + " 'category_id': int(label),\n", + " 'bbox': [x1, y1, x2-x1, y2-y1],\n", + " 'score': float(score)\n", + " })\n", + " if not preds:\n", + " print(\"→ No detections, mAP@0.5=0.0\")\n", + " return 0.0\n", + " with open(\"preds.json\",\"w\") as f:\n", + " json.dump(preds, f)\n", + " coco_dt = coco_gt.loadRes(\"preds.json\")\n", + " ev = COCOeval(coco_gt, coco_dt, iouType=\"bbox\")\n", + " ev.params.imgIds = sorted(coco_gt.getImgIds())\n", + " ev.evaluate(); ev.accumulate(); ev.summarize()\n", + " return ev.stats[1]\n", + "\n", + "@torch.no_grad()\n", + "def select_uncertain(pool_idx, dataset, model, device, k):\n", + " uncertainties = []\n", + " for idx in pool_idx:\n", + " img, _ = dataset[idx]\n", + " out = model([img.to(device)])[0]\n", + " max_score = out['scores'].max().item() if len(out['scores'])>0 else 0.0\n", + " uncertainties.append((1 - max_score, idx))\n", + " uncertainties.sort(reverse=True, key=lambda x: x[0])\n", + " return [idx for _, idx in uncertainties[:k]]\n", + "\n", + "def run_rounds(strategy, dataset, n_classes, coco_gt, device):\n", + " idxs = list(range(len(dataset)))\n", + " random.shuffle(idxs)\n", + " train_idx = idxs[:INITIAL]\n", + " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", + " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", + "\n", + " model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", + " pretrained=True,\n", + " pretrained_backbone=True\n", + " )\n", + " # replace the head for our num classes\n", + " in_features = model.roi_heads.box_predictor.cls_score.in_features\n", + " model.roi_heads.box_predictor = FastRCNNPredictor(in_features, n_classes)\n", + " model.to(device)\n", + "\n", + " opt = torch.optim.SGD(\n", + " model.parameters(),\n", + " lr=0.005,\n", + " momentum=0.9,\n", + " weight_decay=5e-4\n", + " )\n", + "\n", + " sizes, perf = [], []\n", + " for r in range(ROUNDS):\n", + " print(f\"{strategy.title():10s} → Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", + " tr_loader = DataLoader(\n", + " Subset(dataset, train_idx),\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=True,\n", + " collate_fn=collate_fn\n", + " )\n", + " for e in range(EPOCHS_PER_ROUND):\n", + " train_epoch(model, opt, tr_loader, device)\n", + "\n", + " val_loader = DataLoader(\n", + " Subset(dataset, val_idx),\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=False,\n", + " collate_fn=collate_fn\n", + " )\n", + " mAP50 = evaluate_map(model, val_loader, device, coco_gt)\n", + "\n", + " sizes.append(len(train_idx))\n", + " perf.append(mAP50)\n", + "\n", + " if not pool_idx:\n", + " break\n", + " if strategy == 'random':\n", + " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", + " else:\n", + " add = select_uncertain(pool_idx, dataset, model, device,\n", + " min(POOL_BATCH, len(pool_idx)))\n", + " train_idx += add\n", + " pool_idx = [i for i in pool_idx if i not in add]\n", + "\n", + " return sizes, perf\n", + "\n", + "if __name__ == \"__main__\":\n", + " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", + " mapping = {orig: i+1 for i, orig in enumerate(df['label_orig'].unique())}\n", + " df['label'] = df['label_orig'].map(mapping)\n", + " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", + " coco_gt = build_coco_gt(df, GT_JSON)\n", + " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", + " n_classes = df['label'].nunique() + 1 # + background\n", + "\n", + " results = {}\n", + " for strat in SAMPLINGS:\n", + " sizes, perf = run_rounds(strat, dataset, n_classes, coco_gt, DEVICE)\n", + " results[strat] = (sizes, perf)\n", + "\n", + " # plot both curves\n", + " plt.plot(*results['random'], marker='o', label='Random sampling')\n", + " plt.plot(*results['uncertainty'], marker='s', label='Uncertainty sampling')\n", + " plt.xlabel(\"# Training Images\")\n", + " plt.ylabel(\"mAP@0.5\")\n", + " plt.title(\"Daisy Active Learning: Random vs Uncertainty\")\n", + " plt.legend()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db5ce57b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/deepforest/data/flowers/ground_truth.json b/src/deepforest/data/flowers/ground_truth.json new 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HcmV?d00001 From a091dc8946233f8c521d6e463e1a7b70ad62b2dc Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Tue, 19 Aug 2025 19:19:03 +0530 Subject: [PATCH 2/8] add active learning --- src/deepforest/active_learning.py | 319 + .../flowers/Test/1392131677_116ec04751.jpg | Bin 83038 -> 0 bytes .../flowers/Test/1955336401_fbb206d6ef_n.jpg | Bin 31605 -> 0 bytes .../flowers/Test/2346726545_2ebce2b2a6.jpg | Bin 69008 -> 0 bytes src/deepforest/data/flowers/flowers.ipynb | 4336 ----------- src/deepforest/data/flowers/ground_truth.json | 1 - .../0b8942ea-162362897_1d21b70621_m.jpg | Bin 20286 -> 0 bytes .../0cf6ab9b-14147016029_8d3cf2414e.jpg | Bin 108350 -> 0 bytes .../images/0ea84707-5794839_200acd910c_n.jpg | Bin 22855 -> 0 bytes src/deepforest/data/flowers/labels.csv | 398 - src/deepforest/data/flowers/labels_raw.csv | 398 - src/deepforest/data/flowers/preds.json | 1 - src/deepforest/data/flowers/result.json | 6761 ----------------- .../data/flowers/val_vis/img_103.png | Bin 147123 -> 0 bytes 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src/deepforest/data/flowers/preds.json delete mode 100644 src/deepforest/data/flowers/result.json delete mode 100644 src/deepforest/data/flowers/val_vis/img_103.png delete mode 100644 src/deepforest/data/flowers/val_vis/img_75.png create mode 100644 tests/test_active_learning.py diff --git a/src/deepforest/active_learning.py b/src/deepforest/active_learning.py new file mode 100644 index 000000000..1344a821f --- /dev/null +++ b/src/deepforest/active_learning.py @@ -0,0 +1,319 @@ +"""This module provides active learning utilities for the weecology/deepforest +library. + +It includes a Config dataclass for experiment configuration, an +ActiveLearner class that wraps DeepForest's model and training routines, +and an entropy-based acquisition function for selecting unlabeled +images. Training and validation CSV files are expected to follow the +DeepForest format, containing columns for image_path, xmin, ymin, xmax, +ymax, and label. +""" + +import logging +import math +import random +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import pandas as pd +import torch + +import pytorch_lightning as pl +from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint + +from deepforest import main as df_main + + +@dataclass(frozen=True) +class Config: + """Configuration for active learning experiments. + + Attributes: + workdir: Working directory for logs, checkpoints, and acquisitions. + images_dir: Directory containing all image files. + train_csv: CSV with labeled training data (DeepForest format). + val_csv: CSV with labeled validation data. + classes: List of class labels. + + epochs_per_round: Training epochs per active learning round. + batch_size: Training batch size. + lr: Learning rate. + weight_decay: Weight decay for optimizer. + precision: Mixed precision setting (int or str, depending on PL version). + device: Device spec, e.g., "auto", "cpu", "cuda:0". + num_workers: Dataloader workers. + seed: Random seed for reproducibility. + use_release_weights: Whether to warm start from NEON release weights. + + iou_eval: IoU threshold for evaluation. + + k_per_round: Number of images to acquire per round. + score_threshold_pred: Score threshold when generating predictions. + """ + + workdir: str + images_dir: str + train_csv: str + val_csv: str + classes: list + + # Training + epochs_per_round: int = 10 + batch_size: int = 4 + lr: float = 1e-4 + weight_decay: float = 1e-4 + precision: int = 32 + device: str = "auto" + num_workers: int = 4 + seed: int = 42 + use_release_weights: bool = False + + # Evaluation + iou_eval: float = 0.5 + + # Acquisition + k_per_round: int = 50 + score_threshold_pred: float = 0.2 + + +def _seed_everything(seed): + """Seed Python, NumPy, and Torch for reproducibility.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + try: + pl.seed_everything(seed, workers=True) + except Exception: + pass + + +def _resolve_device(device): + """Return (accelerator, devices) tuple understood by PyTorch Lightning.""" + if device == "auto": + return ("gpu", 1) if torch.cuda.is_available() else ("cpu", 1) + if device.startswith("cuda"): + if not torch.cuda.is_available(): + logging.warning("CUDA requested but not available; falling back to CPU.") + return ("cpu", 1) + return ("gpu", 1) + return ("cpu", 1) + + +def _ensure_dir(p): + """Create directory p (and parents) if it does not exist.""" + Path(p).mkdir(parents=True, exist_ok=True) + + +def _read_paths_file(path_or_list): + """Read a file of image paths, or accept a list directly.""" + if isinstance(path_or_list, (list, tuple)): + return [str(Path(p)) for p in path_or_list] + p = Path(path_or_list) + lines = p.read_text(encoding="utf-8").splitlines() + return [ln.strip() for ln in lines if ln.strip()] + + +def _image_entropy_from_predictions(pred_df, classes): + """Compute Shannon entropy from per-class aggregated detection scores. + + Args: + pred_df: DataFrame of predictions (DeepForest format). + classes: List of known class labels. + + Returns: + (entropy, n_preds, mean_score) + - entropy: Shannon entropy of class distribution. + - n_preds: Number of predicted boxes. + - mean_score: Mean detection score. + + Empty predictions receive maximum uncertainty (log(C)). + """ + if pred_df is None or len(pred_df) == 0: + c = max(1, len(classes)) + return (math.log(c), 0, 0.0) + + # Aggregate score mass per class + class_mass = {c: 0.0 for c in classes} + for _, row in pred_df.iterrows(): + label = str(row.get("label", "")) + score = float(row.get("score", 0.0)) + if label in class_mass: + class_mass[label] += max(0.0, min(1.0, score)) + + total = sum(class_mass.values()) + if total <= 0: + c = max(1, len(classes)) + return (math.log(c), len(pred_df), 0.0) + + probs = np.array([class_mass[c] / total for c in classes], dtype=np.float64) + entropy = float(-(probs * np.log(probs + 1e-12)).sum()) + return (entropy, int(len(pred_df)), float(pred_df["score"].mean())) + + +class ActiveLearner: + """High-level wrapper for DeepForest active learning. + + Methods: + fit_one_round() -> Path: Train for one round, return checkpoint path. + evaluate() -> dict: Evaluate model on validation set. + predict_images(paths) -> dict[str, DataFrame]: Run predictions on images. + select_for_labeling(unlabeled_paths, k) -> DataFrame: Rank images by entropy. + """ + + def __init__(self, cfg): + self.cfg = cfg + self.workdir = Path(cfg.workdir) + self.images_dir = Path(cfg.images_dir) + self.train_csv = Path(cfg.train_csv) + self.val_csv = Path(cfg.val_csv) + self.classes = list(cfg.classes) + + _ensure_dir(self.workdir) + _ensure_dir(self.workdir / "logs") + _ensure_dir(self.workdir / "acquisition") + _seed_everything(cfg.seed) + + self.model = self._build_model(cfg) + self.trainer, self._ckpt_cb = self._create_trainer(cfg, self.workdir / "logs") + + self._attach_training_data() + + def _build_model(self, cfg): + """Initialize DeepForest model with correct class count.""" + model = df_main.deepforest() + if cfg.use_release_weights: + model.use_release() + model.config["num_classes"] = len(cfg.classes) + model.config["batch_size"] = cfg.batch_size + return model + + def _create_trainer(self, cfg, log_dir): + """Create PyTorch Lightning Trainer with checkpointing and early + stopping.""" + accelerator, devices = _resolve_device(cfg.device) + ckpt_dir = Path(log_dir) / "checkpoints" + _ensure_dir(ckpt_dir) + + ckpt_cb = ModelCheckpoint( + dirpath=str(ckpt_dir), + filename="epoch{epoch:02d}-val_map", + monitor="val_map", + mode="max", + save_top_k=1, + save_weights_only=True, + auto_insert_metric_name=False, + ) + es_cb = EarlyStopping(monitor="val_map", mode="max", patience=3) + + trainer = pl.Trainer( + max_epochs=cfg.epochs_per_round, + accelerator=accelerator, + devices=devices, + precision=cfg.precision, + default_root_dir=str(log_dir), + callbacks=[ckpt_cb, es_cb], + deterministic=True, + log_every_n_steps=10, + enable_checkpointing=True, + ) + return trainer, ckpt_cb + + def _attach_training_data(self): + """Attach train/val CSVs and root dirs to model config.""" + self.model.config["train"] = self.model.config.get("train", {}) + self.model.config["val"] = self.model.config.get("val", {}) + self.model.config["train"]["csv_file"] = str(self.train_csv) + self.model.config["train"]["root_dir"] = str(self.images_dir) + self.model.config["val"]["csv_file"] = str(self.val_csv) + self.model.config["val"]["root_dir"] = str(self.images_dir) + + def fit_one_round(self): + """Train for one active learning round and return best checkpoint + path.""" + self.model.create_trainer(trainer=self.trainer) + self.trainer.fit(self.model) + ckpt_path = Path( + self._ckpt_cb.best_model_path) if self._ckpt_cb else (self.workdir / "logs" / + "checkpoints") + logging.info("Training finished. Best checkpoint: %s", ckpt_path) + return ckpt_path + + def evaluate(self): + """Run evaluation on validation CSV and return results dict.""" + try: + results = self.model.evaluate(csv_file=str(self.val_csv), + root_dir=str(self.images_dir), + iou_threshold=self.cfg.iou_eval, + predictions=None) + log_summary = {k: v for k, v in results.items() if not hasattr(v, "head")} + logging.info("Evaluation: %s", log_summary) + return dict(results) + except Exception as e: + logging.warning("Evaluation failed: %s", e) + return {} + + def predict_images(self, paths): + """Run predictions for a list of image paths, returning dict[path -> + DataFrame].""" + self.model.eval() + out = {} + for p in paths: + p_str = str(p) + try: + with torch.no_grad(): + df = self.model.predict_image( + image_path=p_str, + return_plot=False, + score_threshold=self.cfg.score_threshold_pred) + if df is None: + df = pd.DataFrame(columns=[ + "xmin", "ymin", "xmax", "ymax", "label", "score", "image_path" + ]) + except Exception as e: + logging.warning("Prediction error for %s: %s", p_str, e) + df = pd.DataFrame(columns=[ + "xmin", "ymin", "xmax", "ymax", "label", "score", "image_path" + ]) + out[p_str] = df + return out + + def select_for_labeling(self, unlabeled_paths, k=None): + """Rank unlabeled images by entropy and return top-k for labeling. + + Args: + unlabeled_paths: List of image paths or file containing paths. + k: Number of images to return (defaults to cfg.k_per_round). + + Returns: + DataFrame with ranked images and entropy scores. + """ + if k is None: + k = self.cfg.k_per_round + + paths = _read_paths_file(unlabeled_paths) + if not paths: + raise ValueError("No unlabeled paths provided") + + logging.info("Acquisition over %d images", len(paths)) + preds = self.predict_images(paths) + + rows = [] + for img_path, df in preds.items(): + ent, n_preds, mean_score = _image_entropy_from_predictions(df, self.classes) + rows.append({ + "image_path": img_path, + "entropy": ent, + "n_preds": 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I tried other models but they were taking too much time and resources, I felt it was more ideal for me to use a very light model just making a basic loop.\n", - "\n", - "Once my loop feels decent, I would change to a heavier and more accurate model\n" - ] - }, - { - "cell_type": "markdown", - "id": "e51bb0c7", - "metadata": {}, - "source": [ - "What is mAP@0.5?\n", - "\n", - "- mAP = mean Average Precision, a standard detection metric\n", - "\n", - "- AP at a single IoU threshold (0.5) = area under the precision–recall curve when a predicted box is “correct” if IoU ≥ 0.5\n", - "\n", - "- mean over classes (here only one “daisy” class) and averaged across images" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "250151f2", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import random\n", - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "from torch.utils.data import Dataset, DataLoader, Subset\n", - "from torchvision.models.detection import fasterrcnn_mobilenet_v3_large_320_fpn\n", - "from torchvision.transforms import functional as F, Resize\n", - "from pycocotools.coco import COCO\n", - "from pycocotools.cocoeval import COCOeval\n", - "from PIL import Image, ImageDraw\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15d4169e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Label map: {0: 1}\n", - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", - " warnings.warn(msg)\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Round 1/5, train=3\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Round 2/5, train=4\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - "Round 3/5, train=5\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - "Round 4/5, train=6\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Round 5/5, train=7\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inference done → test_results/\n" - ] - } - ], - "source": [ - "\n", - "\n", - "# ---- Settings ----\n", - "TRAIN_JSON = \"result.json\" \n", - "TRAIN_DIR = \"images\" \n", - "TEST_DIR = \"Test\" \n", - "CSV_FILE = \"labels.csv\"\n", - "GT_JSON = \"ground_truth.json\"\n", - "DEVICE = torch.device(\"cpu\") \n", - "\n", - "RESIZE = Resize((320, 320))\n", - "BATCH_SIZE = 1\n", - "INITIAL = 3\n", - "VAL_SIZE = 2\n", - "POOL_BATCH = 1\n", - "ROUNDS = 5\n", - "\n", - "# ---- Dataset ----\n", - "class DaisyDataset(Dataset):\n", - " def __init__(self, csv, img_dir):\n", - " self.df = pd.read_csv(csv)\n", - " self.img_dir = img_dir\n", - " self.imgs = self.df['image_path'].unique().tolist()\n", - " def __len__(self):\n", - " return len(self.imgs)\n", - " def __getitem__(self, idx):\n", - " fn = self.imgs[idx]\n", - " img = Image.open(os.path.join(self.img_dir, fn)).convert(\"RGB\")\n", - " img = RESIZE(img)\n", - " recs = self.df[self.df['image_path'] == fn]\n", - " boxes = recs[['xmin','ymin','xmax','ymax']].values.astype(np.float32)\n", - " labels = recs['label'].values.astype(np.int64)\n", - " target = {\n", - " 'boxes': torch.from_numpy(boxes),\n", - " 'labels': torch.from_numpy(labels),\n", - " 'image_id': torch.tensor([idx])\n", - " }\n", - " return F.to_tensor(img), target\n", - "\n", - "def collate_fn(batch):\n", - " return tuple(zip(*batch))\n", - "\n", - "# ---- Parse COCO → CSV ----\n", - "def parse_coco(json_file, img_dir):\n", - " coco = COCO(json_file)\n", - " records = []\n", - " for ann in coco.loadAnns(coco.getAnnIds()):\n", - " img = coco.loadImgs(ann['image_id'])[0]\n", - " fn = os.path.basename(img['file_name'])\n", - " x,y,w,h = ann['bbox']\n", - " records.append({\n", - " 'image_path': fn,\n", - " 'xmin': x, 'ymin': y,\n", - " 'xmax': x+w, 'ymax': y+h,\n", - " 'label_orig': ann['category_id']\n", - " })\n", - " df = pd.DataFrame(records)\n", - " df.to_csv(\"labels_raw.csv\", index=False)\n", - " return df\n", - "\n", - "# ---- Build COCO GT JSON ----\n", - "def build_coco_gt(df, out_json):\n", - " images, anns, cats = [], [], []\n", - " uniq = df['image_path'].unique().tolist()\n", - " # images\n", - " for i, fn in enumerate(uniq):\n", - " images.append({\n", - " 'id': int(i),\n", - " 'file_name': fn\n", - " })\n", - " # annotations\n", - " aid = 1\n", - " for _, r in df.iterrows():\n", - " x1 = int(r['xmin'])\n", - " y1 = int(r['ymin'])\n", - " x2 = int(r['xmax'])\n", - " y2 = int(r['ymax'])\n", - " w = x2 - x1\n", - " h = y2 - y1\n", - " anns.append({\n", - " 'id': int(aid),\n", - " 'image_id': int(uniq.index(r['image_path'])),\n", - " 'category_id': int(r['label']),\n", - " 'bbox': [x1, y1, w, h],\n", - " 'area': float(w * h),\n", - " 'iscrowd': 0\n", - " })\n", - " aid += 1\n", - " # categories\n", - " for cid in sorted(df['label'].unique()):\n", - " cats.append({\n", - " 'id': int(cid),\n", - " 'name': str(cid)\n", - " })\n", - " coco_dict = {\n", - " 'info': {},\n", - " 'licenses': [],\n", - " 'images': images,\n", - " 'annotations': anns,\n", - " 'categories': cats\n", - " }\n", - " # write out\n", - " with open(out_json, 'w') as f:\n", - " json.dump(coco_dict, f)\n", - " return COCO(out_json)\n", - "\n", - "\n", - "# ---- Training & Eval ----\n", - "def train_epoch(model, opt, loader, device):\n", - " model.train()\n", - " for imgs, tgts in loader:\n", - " imgs = [i.to(device) for i in imgs]\n", - " tgts = [{k:v.to(device) for k,v in t.items()} for t in tgts]\n", - " losses = model(imgs, tgts)\n", - " loss = sum(losses.values())\n", - " opt.zero_grad()\n", - " loss.backward()\n", - " opt.step()\n", - "\n", - "@torch.no_grad()\n", - "def evaluate_map(model, loader, device, coco_gt):\n", - " model.eval()\n", - " preds = []\n", - " for imgs, tgts in loader:\n", - " imgs = [i.to(device) for i in imgs]\n", - " outs = model(imgs)\n", - " for tgt, out in zip(tgts, outs):\n", - " img_id = int(tgt['image_id'].item())\n", - " for box, score, label in zip(out['boxes'].cpu(), out['scores'].cpu(), out['labels'].cpu()):\n", - " x1,y1,x2,y2 = box.tolist()\n", - " preds.append({\n", - " 'image_id': img_id,\n", - " 'category_id': int(label),\n", - " 'bbox': [x1,y1,x2-x1,y2-y1],\n", - " 'score': float(score)\n", - " })\n", - " if not preds:\n", - " print(\"→ No detections, mAP@0.5=0.0\")\n", - " return 0.0\n", - " with open(\"preds.json\",\"w\") as f:\n", - " json.dump(preds, f)\n", - " coco_dt = coco_gt.loadRes(\"preds.json\")\n", - " ev = COCOeval(coco_gt, coco_dt, iouType=\"bbox\")\n", - " ev.params.imgIds = sorted(coco_gt.getImgIds())\n", - " ev.evaluate(); ev.accumulate(); ev.summarize()\n", - " return ev.stats[1]\n", - "\n", - "# ---- Main ----\n", - "def main():\n", - " # 1) parse COCO → raw labels\n", - " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", - " # 2) remap labels → \"Daisy\" -> 1 \n", - " mapping = {orig: i+1 for i,orig in enumerate(df['label_orig'].unique())}\n", - " print(\"Label map:\", mapping)\n", - " df['label'] = df['label_orig'].map(mapping)\n", - " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", - " # 3) build COCO GT\n", - " coco_gt = build_coco_gt(df, GT_JSON)\n", - "\n", - " # 4) prepare splits\n", - " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", - " n_classes = df['label'].nunique() + 1\n", - " idxs = list(range(len(dataset)))\n", - " random.shuffle(idxs)\n", - " train_idx = idxs[:INITIAL]\n", - " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", - " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", - "\n", - " # 5) model & optimizer\n", - " model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", - " pretrained=False,\n", - " pretrained_backbone=True,\n", - " num_classes=n_classes\n", - " ).to(DEVICE)\n", - " opt = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", - "\n", - " # 6) active learning loop\n", - " perf, sizes = [], []\n", - " for r in range(ROUNDS):\n", - " print(f\"Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", - " tr_loader = DataLoader(Subset(dataset, train_idx), batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", - " val_loader= DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False,collate_fn=collate_fn)\n", - "\n", - " train_epoch(model, opt, tr_loader, DEVICE)\n", - " mAP50 = evaluate_map(model, val_loader, DEVICE, coco_gt)\n", - " perf.append(mAP50); sizes.append(len(train_idx))\n", - "\n", - " if pool_idx:\n", - " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", - " train_idx += add\n", - " pool_idx = [i for i in pool_idx if i not in add]\n", - " else:\n", - " break\n", - "\n", - " # 7) plot\n", - " plt.plot(sizes, perf, marker='o')\n", - " plt.xlabel(\"# Training Images\"); plt.ylabel(\"mAP@0.5\")\n", - " plt.title(\"Daisy Active Learning\"); plt.grid(True)\n", - " plt.show()\n", - "\n", - " # 8) inference on TEST_DIR\n", - " os.makedirs(\"test_results\", exist_ok=True)\n", - " model.eval()\n", - " for fn in sorted(os.listdir(TEST_DIR)):\n", - " if not fn.lower().endswith((\".jpg\",\".png\")): continue\n", - " img = Image.open(os.path.join(TEST_DIR,fn)).convert(\"RGB\")\n", - " img = RESIZE(img)\n", - " t = F.to_tensor(img).to(DEVICE)\n", - " out = model([t])[0]\n", - " draw = ImageDraw.Draw(img)\n", - " for box, score in zip(out['boxes'].cpu(), out['scores'].cpu()):\n", - " if score < 0.5: continue\n", - " x1,y1,x2,y2 = box.tolist()\n", - " draw.rectangle([x1,y1,x2,y2], outline=\"red\", width=2)\n", - " draw.text((x1,y1-10), f\"{score:.2f}\", fill=\"red\")\n", - " img.save(os.path.join(\"test_results\", fn))\n", - " print(\"Inference done → test_results/\")\n", - "\n", - "if __name__ == \"__main__\":\n", - " main()" - ] - }, - { - "cell_type": "markdown", - "id": "9e61717e", - "metadata": {}, - "source": [ - "# Next Steps\n" - ] - }, - { - "cell_type": "markdown", - "id": "c8b9e363", - "metadata": {}, - "source": [ - "\n", - "\n", - "- Plot a longer active learning curve\n", - " - Run more rounds (e.g., up to 20–30 samples used), andplot mAP vs training size. That’s when performance shouldstart climbing meaningfully.\n", - "- Try a baseline using all 131 images\n", - " - Train the model using more data and evaluate — this givesyou an upper bound on achievable mAP@0.5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9616c0f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.01s)\n", - "creating index...\n", - "index created!\n", - "Label map: {0: 1}\n", - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", - " warnings.warn(msg)\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Round 1/28, train=3\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.03s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " mAP@0.5 = 0.0001\n", - "Round 2/28, train=4\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 3/28, train=5\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 4/28, train=6\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 5/28, train=7\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 6/28, train=8\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 7/28, train=9\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 8/28, train=10\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0050\n", - "Round 9/28, train=11\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 10/28, train=12\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 11/28, train=13\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 12/28, train=14\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " mAP@0.5 = 0.0025\n", - "Round 13/28, train=15\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " mAP@0.5 = 0.0025\n", - "Round 14/28, train=16\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " mAP@0.5 = 0.0033\n", - "Round 15/28, train=17\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 16/28, train=18\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " mAP@0.5 = 0.0033\n", - "Round 17/28, train=19\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0050\n", - "Round 18/28, train=20\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0050\n", - "Round 19/28, train=21\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0017\n", - "Round 20/28, train=22\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0050\n", - "Round 21/28, train=23\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " mAP@0.5 = 0.0099\n", - "Round 22/28, train=24\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0025\n", - "Round 23/28, train=25\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " mAP@0.5 = 0.0033\n", - "Round 24/28, train=26\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0017\n", - "Round 25/28, train=27\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " mAP@0.5 = 0.0033\n", - "Round 26/28, train=28\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " mAP@0.5 = 0.0025\n", - "Round 27/28, train=29\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 28/28, train=30\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Baseline training on full dataset...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", - " warnings.warn(msg)\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Baseline epoch 1/5\n", - " Baseline epoch 2/5\n", - " Baseline epoch 3/5\n", - " Baseline epoch 4/5\n", - " Baseline epoch 5/5\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Baseline mAP@0.5 on validation set: 0.0099\n", - "Inference done → test_results/\n" - ] - } - ], - "source": [ - "# ---- Settings ----\n", - "TRAIN_JSON = \"result.json\" \n", - "TRAIN_DIR = \"images\" \n", - "TEST_DIR = \"Test\" \n", - "CSV_FILE = \"labels.csv\"\n", - "GT_JSON = \"ground_truth.json\"\n", - "DEVICE = torch.device(\"cpu\") \n", - "\n", - "RESIZE = Resize((320, 320))\n", - "BATCH_SIZE = 1\n", - "INITIAL = 3\n", - "VAL_SIZE = 2\n", - "POOL_BATCH = 1\n", - "ROUNDS = 28 # run up to ~30 training samples\n", - "\n", - "\n", - "# ---- Main ----\n", - "def main():\n", - " # 1) parse COCO → raw labels\n", - " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", - " # 2) remap labels → 1…N\n", - " mapping = {orig: i+1 for i,orig in enumerate(df['label_orig'].unique())}\n", - " print(\"Label map:\", mapping)\n", - " df['label'] = df['label_orig'].map(mapping)\n", - " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", - " # 3) build COCO GT\n", - " coco_gt = build_coco_gt(df, GT_JSON)\n", - "\n", - " # 4) prepare splits\n", - " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", - " n_classes = df['label'].nunique() + 1\n", - " idxs = list(range(len(dataset)))\n", - " random.shuffle(idxs)\n", - " train_idx = idxs[:INITIAL]\n", - " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", - " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", - "\n", - " # 5) model & optimizer\n", - " model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", - " pretrained=False,\n", - " pretrained_backbone=True,\n", - " num_classes=n_classes\n", - " ).to(DEVICE)\n", - " opt = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", - "\n", - " # 6) active learning loop\n", - " perf, sizes = [], []\n", - " for r in range(ROUNDS):\n", - " print(f\"Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", - " tr_loader = DataLoader(Subset(dataset, train_idx), batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", - " val_loader= DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False,collate_fn=collate_fn)\n", - "\n", - " train_epoch(model, opt, tr_loader, DEVICE)\n", - " mAP50 = evaluate_map(model, val_loader, DEVICE, coco_gt)\n", - " print(f\" mAP@0.5 = {mAP50:.4f}\")\n", - " perf.append(mAP50); sizes.append(len(train_idx))\n", - "\n", - " if pool_idx:\n", - " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", - " train_idx += add\n", - " pool_idx = [i for i in pool_idx if i not in add]\n", - " else:\n", - " break\n", - "\n", - " # 7) plot\n", - " plt.plot(sizes, perf, marker='o')\n", - " plt.xlabel(\"# Training Images\"); plt.ylabel(\"mAP@0.5\")\n", - " plt.title(\"Daisy Active Learning\"); plt.grid(True); plt.show()\n", - "\n", - " # 8) baseline on full dataset\n", - " print(\"Baseline training on full dataset...\")\n", - " base_model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", - " pretrained=False,\n", - " pretrained_backbone=True,\n", - " num_classes=n_classes\n", - " ).to(DEVICE)\n", - " base_opt = torch.optim.SGD(base_model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", - " full_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", - " for ep in range(5):\n", - " print(f\" Baseline epoch {ep+1}/5\")\n", - " train_epoch(base_model, base_opt, full_loader, DEVICE)\n", - " base_mAP = evaluate_map(\n", - " base_model,\n", - " DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn),\n", - " DEVICE, coco_gt\n", - " )\n", - " print(f\"Baseline mAP@0.5 on validation set: {base_mAP:.4f}\")\n", - "\n", - " # 9) inference on TEST_DIR\n", - " os.makedirs(\"test_results\", exist_ok=True)\n", - " model.eval()\n", - " for fn in sorted(os.listdir(TEST_DIR)):\n", - " if not fn.lower().endswith((\".jpg\",\".png\")): continue\n", - " img = Image.open(os.path.join(TEST_DIR,fn)).convert(\"RGB\")\n", - " img = RESIZE(img)\n", - " t = F.to_tensor(img).to(DEVICE)\n", - " out = model([t])[0]\n", - " draw = ImageDraw.Draw(img)\n", - " for box, score in zip(out['boxes'].cpu(), out['scores'].cpu()):\n", - " if score < 0.5: continue\n", - " x1,y1,x2,y2 = box.tolist()\n", - " draw.rectangle([x1,y1,x2,y2], outline=\"red\", width=2)\n", - " draw.text((x1,y1-10), f\"{score:.2f}\", fill=\"red\")\n", - " img.save(os.path.join(\"test_results\", fn))\n", - " print(\"Inference done → test_results/\")\n", - "\n", - "if __name__ == \"__main__\":\n", - " main()\n" - ] - }, - { - "cell_type": "markdown", - "id": "a5a03577", - "metadata": {}, - "source": [ - "# INTRODUCED UNCERTAINTY SAMPLINGS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e4d5ace", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Label map: {0: 1}\n", - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", - " warnings.warn(msg)\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Round 1/14, train=3\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.03s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " mAP@0.5 = 0.0010\n", - "Round 2/14, train=4\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " mAP@0.5 = 0.0000\n", - "Round 3/14, train=5\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n", - " mAP@0.5 = 0.0011\n", - "Round 4/14, train=6\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.03s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " mAP@0.5 = 0.0010\n", - "Round 5/14, train=7\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " mAP@0.5 = 0.0012\n", - "Round 6/14, train=8\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " mAP@0.5 = 0.0099\n", - "Round 7/14, train=9\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0099\n", - "Round 8/14, train=10\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " mAP@0.5 = 0.0009\n", - "Round 9/14, train=11\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " mAP@0.5 = 0.0099\n", - "Round 10/14, train=12\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " mAP@0.5 = 0.0034\n", - "Round 11/14, train=13\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.011\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " mAP@0.5 = 0.0113\n", - "Round 12/14, train=14\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0099\n", - "Round 13/14, train=15\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " mAP@0.5 = 0.0099\n", - "Round 14/14, train=16\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.011\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " mAP@0.5 = 0.0106\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# ---- Settings ----\n", - "TRAIN_JSON = \"result.json\"\n", - "TRAIN_DIR = \"images\"\n", - "TEST_DIR = \"Test\"\n", - "CSV_FILE = \"labels.csv\"\n", - "GT_JSON = \"ground_truth.json\"\n", - "DEVICE = torch.device(\"cpu\")\n", - "RESIZE = Resize((320, 320))\n", - "BATCH_SIZE = 1\n", - "INITIAL = 3\n", - "VAL_SIZE = 2\n", - "POOL_BATCH = 1\n", - "ROUNDS = 14\n", - "\n", - "# sampling strategy: 'random' or 'uncertainty'\n", - "SAMPLING = 'uncertainty'\n", - "\n", - "class DaisyDataset(Dataset):\n", - " def __init__(self, csv, img_dir):\n", - " self.df = pd.read_csv(csv)\n", - " self.img_dir = img_dir\n", - " self.imgs = self.df['image_path'].unique().tolist()\n", - " def __len__(self):\n", - " return len(self.imgs)\n", - " def __getitem__(self, idx):\n", - " fn = self.imgs[idx]\n", - " img = Image.open(os.path.join(self.img_dir, fn)).convert(\"RGB\")\n", - " img = RESIZE(img)\n", - " recs = self.df[self.df['image_path'] == fn]\n", - " boxes = recs[['xmin','ymin','xmax','ymax']].values.astype(np.float32)\n", - " labels = recs['label'].values.astype(np.int64)\n", - " target = {'boxes': torch.from_numpy(boxes), 'labels': torch.from_numpy(labels), 'image_id': torch.tensor([idx])}\n", - " return F.to_tensor(img), target\n", - "\n", - "def collate_fn(batch):\n", - " return tuple(zip(*batch))\n", - "\n", - "def parse_coco(json_file, img_dir):\n", - " coco = COCO(json_file)\n", - " records = []\n", - " for ann in coco.loadAnns(coco.getAnnIds()):\n", - " img = coco.loadImgs(ann['image_id'])[0]\n", - " fn = os.path.basename(img['file_name'])\n", - " x,y,w,h = ann['bbox']\n", - " records.append({'image_path': fn, 'xmin': x, 'ymin': y, 'xmax': x+w, 'ymax': y+h, 'label_orig': ann['category_id']})\n", - " df = pd.DataFrame(records)\n", - " df.to_csv(\"labels_raw.csv\", index=False)\n", - " return df\n", - "\n", - "def build_coco_gt(df, out_json):\n", - " images, anns, cats = [], [], []\n", - " uniq = df['image_path'].unique().tolist()\n", - " for i, fn in enumerate(uniq): images.append({'id': i, 'file_name': fn})\n", - " aid = 1\n", - " for _, r in df.iterrows():\n", - " x1,y1,x2,y2 = map(int, [r['xmin'], r['ymin'], r['xmax'], r['ymax']])\n", - " w, h = x2-x1, y2-y1\n", - " anns.append({'id': aid, 'image_id': uniq.index(r['image_path']), 'category_id': int(r['label']), 'bbox': [x1,y1,w,h], 'area': float(w*h), 'iscrowd': 0})\n", - " aid += 1\n", - " for cid in sorted(df['label'].unique()): cats.append({'id': int(cid), 'name': str(cid)})\n", - " coco_dict = {'info':{}, 'licenses':[], 'images':images, 'annotations':anns, 'categories':cats}\n", - " with open(out_json, 'w') as f: json.dump(coco_dict, f)\n", - " return COCO(out_json)\n", - "\n", - "def train_epoch(model, opt, loader, device):\n", - " model.train()\n", - " for imgs, tgts in loader:\n", - " imgs = [i.to(device) for i in imgs]\n", - " tgts = [{k:v.to(device) for k,v in t.items()} for t in tgts]\n", - " losses = model(imgs, tgts)\n", - " loss = sum(losses.values())\n", - " opt.zero_grad()\n", - " loss.backward()\n", - " opt.step()\n", - "\n", - "@torch.no_grad()\n", - "def evaluate_map(model, loader, device, coco_gt):\n", - " model.eval()\n", - " preds = []\n", - " for imgs, tgts in loader:\n", - " imgs = [i.to(device) for i in imgs]\n", - " outs = model(imgs)\n", - " for tgt, out in zip(tgts, outs):\n", - " img_id = int(tgt['image_id'].item())\n", - " for box, score, label in zip(out['boxes'].cpu(), out['scores'].cpu(), out['labels'].cpu()):\n", - " x1,y1,x2,y2 = box.tolist()\n", - " preds.append({'image_id': img_id, 'category_id': int(label), 'bbox': [x1,y1,x2-x1,y2-y1], 'score': float(score)})\n", - " if not preds:\n", - " print(\"→ No detections, mAP@0.5=0.0\")\n", - " return 0.0\n", - " with open(\"preds.json\",\"w\") as f: json.dump(preds, f)\n", - " coco_dt = coco_gt.loadRes(\"preds.json\")\n", - " ev = COCOeval(coco_gt, coco_dt, iouType=\"bbox\")\n", - " ev.params.imgIds = sorted(coco_gt.getImgIds())\n", - " ev.evaluate(); ev.accumulate(); ev.summarize()\n", - " return ev.stats[1]\n", - "\n", - "@torch.no_grad()\n", - "def select_uncertain(pool_idx, dataset, model, device, k):\n", - " uncertainties = []\n", - " for idx in pool_idx:\n", - " img, _ = dataset[idx]\n", - " out = model([img.to(device)])[0]\n", - " max_score = out['scores'].max().item() if len(out['scores'])>0 else 0.0\n", - " uncertainties.append((1 - max_score, idx))\n", - " uncertainties.sort(reverse=True, key=lambda x: x[0])\n", - " return [idx for _, idx in uncertainties[:k]]\n", - "\n", - "def main():\n", - " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", - " mapping = {orig: i+1 for i, orig in enumerate(df['label_orig'].unique())}\n", - " print(\"Label map:\", mapping)\n", - " df['label'] = df['label_orig'].map(mapping)\n", - " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", - " coco_gt = build_coco_gt(df, GT_JSON)\n", - "\n", - " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", - " n_classes = df['label'].nunique() + 1\n", - " idxs = list(range(len(dataset)))\n", - " random.shuffle(idxs)\n", - " train_idx = idxs[:INITIAL]\n", - " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", - " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", - "\n", - " model = fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, pretrained_backbone=True, num_classes=n_classes).to(DEVICE)\n", - " opt = torch.optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-4)\n", - "\n", - " perf, sizes = [], []\n", - " for r in range(ROUNDS):\n", - " print(f\"Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", - " tr_loader = DataLoader(Subset(dataset, train_idx), batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)\n", - " val_loader= DataLoader(Subset(dataset, val_idx), batch_size=BATCH_SIZE, shuffle=False, collate_fn=collate_fn)\n", - "\n", - " train_epoch(model, opt, tr_loader, DEVICE)\n", - " mAP50 = evaluate_map(model, val_loader, DEVICE, coco_gt)\n", - " print(f\" mAP@0.5 = {mAP50:.4f}\")\n", - " perf.append(mAP50); sizes.append(len(train_idx))\n", - "\n", - " if not pool_idx:\n", - " break\n", - "\n", - " if SAMPLING == 'random':\n", - " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", - " else:\n", - " add = select_uncertain(pool_idx, dataset, model, DEVICE, min(POOL_BATCH, len(pool_idx)))\n", - " train_idx += add\n", - " pool_idx = [i for i in pool_idx if i not in add]\n", - "\n", - " plt.plot(sizes, perf, marker='o', label=SAMPLING)\n", - " plt.xlabel(\"# Training Images\"); plt.ylabel(\"mAP@0.5\")\n", - " plt.title(\"Daisy Active Learning\"); plt.legend(); plt.show()\n", - "\n", - "if __name__ == \"__main__\":\n", - " main()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "eddfc36b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.00s)\n", - "creating index...\n", - "index created!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Random → Round 1/48, train=3\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - "Random → Round 2/48, train=4\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - "Random → Round 3/48, train=5\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 4/48, train=6\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 5/48, train=7\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Random → Round 6/48, train=8\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Random → Round 7/48, train=9\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 8/48, train=10\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 9/48, train=11\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 10/48, train=12\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 11/48, train=13\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - "Random → Round 12/48, train=14\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 13/48, train=15\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - "Random → Round 14/48, train=16\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 15/48, train=17\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Random → Round 16/48, train=18\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 17/48, train=19\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 18/48, train=20\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 19/48, train=21\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 20/48, train=22\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 21/48, train=23\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 22/48, train=24\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 23/48, train=25\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - "Random → Round 24/48, train=26\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 25/48, train=27\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 26/48, train=28\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 27/48, train=29\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Random → Round 28/48, train=30\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 29/48, train=31\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 30/48, train=32\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Random → Round 31/48, train=33\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 32/48, train=34\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 33/48, train=35\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 34/48, train=36\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 35/48, train=37\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 36/48, train=38\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 37/48, train=39\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 38/48, train=40\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 39/48, train=41\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 40/48, train=42\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 41/48, train=43\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 42/48, train=44\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 43/48, train=45\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 44/48, train=46\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 45/48, train=47\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Random → Round 46/48, train=48\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 47/48, train=49\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Random → Round 48/48, train=50\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1`. You can also use `weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained_backbone' is deprecated since 0.13 and may be removed in the future, please use 'weights_backbone' instead.\n", - " warnings.warn(\n", - "/home/nakshatra/Documents/gsoc/DeepForest/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights_backbone' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights_backbone=MobileNet_V3_Large_Weights.IMAGENET1K_V1`. You can also use `weights_backbone=MobileNet_V3_Large_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Uncertainty → Round 1/48, train=3\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 2/48, train=4\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 3/48, train=5\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 4/48, train=6\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 5/48, train=7\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 6/48, train=8\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - "Uncertainty → Round 7/48, train=9\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - "Uncertainty → Round 8/48, train=10\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 9/48, train=11\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 10/48, train=12\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 11/48, train=13\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 12/48, train=14\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.03s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 13/48, train=15\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 14/48, train=16\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 15/48, train=17\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - "Uncertainty → Round 16/48, train=18\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 17/48, train=19\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 18/48, train=20\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 19/48, train=21\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - "Uncertainty → Round 20/48, train=22\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 21/48, train=23\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 22/48, train=24\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Uncertainty → Round 23/48, train=25\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 24/48, train=26\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n", - "Uncertainty → Round 25/48, train=27\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Uncertainty → Round 26/48, train=28\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 27/48, train=29\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 28/48, train=30\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 29/48, train=31\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 30/48, train=32\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Uncertainty → Round 31/48, train=33\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 32/48, train=34\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 33/48, train=35\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - "Uncertainty → Round 34/48, train=36\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 35/48, train=37\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 36/48, train=38\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 37/48, train=39\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.03s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Uncertainty → Round 38/48, train=40\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 39/48, train=41\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Uncertainty → Round 40/48, train=42\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - "Uncertainty → Round 41/48, train=43\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 42/48, train=44\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 43/48, train=45\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.03s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.006\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 44/48, train=46\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.008\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n", - "Uncertainty → Round 45/48, train=47\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n", - "Uncertainty → Round 46/48, train=48\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.007\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - "Uncertainty → Round 47/48, train=49\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.003\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n", - "Uncertainty → Round 48/48, train=50\n", - "Loading and preparing results...\n", - "DONE (t=0.00s)\n", - "creating index...\n", - "index created!\n", - "Running per image evaluation...\n", - "Evaluate annotation type *bbox*\n", - "DONE (t=0.02s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.004\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.010\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import os\n", - "import random\n", - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "from torch.utils.data import Dataset, DataLoader, Subset\n", - "from torchvision.models.detection import fasterrcnn_mobilenet_v3_large_320_fpn\n", - "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n", - "from torchvision.transforms import functional as F, Resize\n", - "from pycocotools.coco import COCO\n", - "from pycocotools.cocoeval import COCOeval\n", - "from PIL import Image\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# ---- Settings ----\n", - "TRAIN_JSON = \"result.json\"\n", - "TRAIN_DIR = \"images\"\n", - "CSV_FILE = \"labels.csv\"\n", - "GT_JSON = \"ground_truth.json\"\n", - "DEVICE = torch.device(\"cpu\")\n", - "RESIZE = Resize((320, 320))\n", - "BATCH_SIZE = 1\n", - "INITIAL = 3\n", - "VAL_SIZE = 2\n", - "POOL_BATCH = 1\n", - "ROUNDS = 48\n", - "EPOCHS_PER_ROUND = 5\n", - "SAMPLINGS = ['random', 'uncertainty']\n", - "\n", - "class DaisyDataset(Dataset):\n", - " def __init__(self, csv, img_dir):\n", - " self.df = pd.read_csv(csv)\n", - " self.img_dir = img_dir\n", - " self.imgs = self.df['image_path'].unique().tolist()\n", - "\n", - " def __len__(self):\n", - " return len(self.imgs)\n", - "\n", - " def __getitem__(self, idx):\n", - " fn = self.imgs[idx]\n", - " img = Image.open(os.path.join(self.img_dir, fn)).convert(\"RGB\")\n", - " img = RESIZE(img)\n", - " recs = self.df[self.df['image_path'] == fn]\n", - " boxes = recs[['xmin','ymin','xmax','ymax']].values.astype(np.float32)\n", - " labels = recs['label'].values.astype(np.int64)\n", - " target = {\n", - " 'boxes': torch.from_numpy(boxes),\n", - " 'labels': torch.from_numpy(labels),\n", - " 'image_id': torch.tensor([idx])\n", - " }\n", - " return F.to_tensor(img), target\n", - "\n", - "def collate_fn(batch):\n", - " return tuple(zip(*batch))\n", - "\n", - "def parse_coco(json_file, img_dir):\n", - " coco = COCO(json_file)\n", - " records = []\n", - " for ann in coco.loadAnns(coco.getAnnIds()):\n", - " img = coco.loadImgs(ann['image_id'])[0]\n", - " fn = os.path.basename(img['file_name'])\n", - " x,y,w,h = ann['bbox']\n", - " records.append({\n", - " 'image_path': fn,\n", - " 'xmin': x,\n", - " 'ymin': y,\n", - " 'xmax': x+w,\n", - " 'ymax': y+h,\n", - " 'label_orig': ann['category_id']\n", - " })\n", - " df = pd.DataFrame(records)\n", - " return df\n", - "\n", - "def build_coco_gt(df, out_json):\n", - " images, anns, cats = [], [], []\n", - " uniq = df['image_path'].unique().tolist()\n", - " for i, fn in enumerate(uniq):\n", - " images.append({'id': i, 'file_name': fn})\n", - " aid = 1\n", - " for _, r in df.iterrows():\n", - " x1,y1,x2,y2 = map(int, [r['xmin'], r['ymin'], r['xmax'], r['ymax']])\n", - " w, h = x2-x1, y2-y1\n", - " anns.append({\n", - " 'id': aid,\n", - " 'image_id': uniq.index(r['image_path']),\n", - " 'category_id': int(r['label']),\n", - " 'bbox': [x1, y1, w, h],\n", - " 'area': float(w*h),\n", - " 'iscrowd': 0\n", - " })\n", - " aid += 1\n", - " for cid in sorted(df['label'].unique()):\n", - " cats.append({'id': int(cid), 'name': str(cid)})\n", - " coco_dict = {\n", - " 'info': {}, 'licenses': [], \n", - " 'images': images, \n", - " 'annotations': anns, \n", - " 'categories': cats\n", - " }\n", - " with open(out_json, 'w') as f:\n", - " json.dump(coco_dict, f)\n", - " return COCO(out_json)\n", - "\n", - "def train_epoch(model, opt, loader, device):\n", - " model.train()\n", - " for imgs, tgts in loader:\n", - " imgs = [i.to(device) for i in imgs]\n", - " tgts = [{k: v.to(device) for k, v in t.items()} for t in tgts]\n", - " losses = model(imgs, tgts)\n", - " loss = sum(losses.values())\n", - " opt.zero_grad()\n", - " loss.backward()\n", - " opt.step()\n", - "\n", - "@torch.no_grad()\n", - "def evaluate_map(model, loader, device, coco_gt):\n", - " model.eval()\n", - " preds = []\n", - " for imgs, tgts in loader:\n", - " imgs = [i.to(device) for i in imgs]\n", - " outs = model(imgs)\n", - " for tgt, out in zip(tgts, outs):\n", - " img_id = int(tgt['image_id'].item())\n", - " for box, score, label in zip(\n", - " out['boxes'].cpu(),\n", - " out['scores'].cpu(),\n", - " out['labels'].cpu()\n", - " ):\n", - " x1,y1,x2,y2 = box.tolist()\n", - " preds.append({\n", - " 'image_id': img_id,\n", - " 'category_id': int(label),\n", - " 'bbox': [x1, y1, x2-x1, y2-y1],\n", - " 'score': float(score)\n", - " })\n", - " if not preds:\n", - " print(\"→ No detections, mAP@0.5=0.0\")\n", - " return 0.0\n", - " with open(\"preds.json\",\"w\") as f:\n", - " json.dump(preds, f)\n", - " coco_dt = coco_gt.loadRes(\"preds.json\")\n", - " ev = COCOeval(coco_gt, coco_dt, iouType=\"bbox\")\n", - " ev.params.imgIds = sorted(coco_gt.getImgIds())\n", - " ev.evaluate(); ev.accumulate(); ev.summarize()\n", - " return ev.stats[1]\n", - "\n", - "@torch.no_grad()\n", - "def select_uncertain(pool_idx, dataset, model, device, k):\n", - " uncertainties = []\n", - " for idx in pool_idx:\n", - " img, _ = dataset[idx]\n", - " out = model([img.to(device)])[0]\n", - " max_score = out['scores'].max().item() if len(out['scores'])>0 else 0.0\n", - " uncertainties.append((1 - max_score, idx))\n", - " uncertainties.sort(reverse=True, key=lambda x: x[0])\n", - " return [idx for _, idx in uncertainties[:k]]\n", - "\n", - "def run_rounds(strategy, dataset, n_classes, coco_gt, device):\n", - " idxs = list(range(len(dataset)))\n", - " random.shuffle(idxs)\n", - " train_idx = idxs[:INITIAL]\n", - " val_idx = idxs[INITIAL:INITIAL+VAL_SIZE]\n", - " pool_idx = idxs[INITIAL+INITIAL+VAL_SIZE:]\n", - "\n", - " model = fasterrcnn_mobilenet_v3_large_320_fpn(\n", - " pretrained=True,\n", - " pretrained_backbone=True\n", - " )\n", - " # replace the head for our num classes\n", - " in_features = model.roi_heads.box_predictor.cls_score.in_features\n", - " model.roi_heads.box_predictor = FastRCNNPredictor(in_features, n_classes)\n", - " model.to(device)\n", - "\n", - " opt = torch.optim.SGD(\n", - " model.parameters(),\n", - " lr=0.005,\n", - " momentum=0.9,\n", - " weight_decay=5e-4\n", - " )\n", - "\n", - " sizes, perf = [], []\n", - " for r in range(ROUNDS):\n", - " print(f\"{strategy.title():10s} → Round {r+1}/{ROUNDS}, train={len(train_idx)}\")\n", - " tr_loader = DataLoader(\n", - " Subset(dataset, train_idx),\n", - " batch_size=BATCH_SIZE,\n", - " shuffle=True,\n", - " collate_fn=collate_fn\n", - " )\n", - " for e in range(EPOCHS_PER_ROUND):\n", - " train_epoch(model, opt, tr_loader, device)\n", - "\n", - " val_loader = DataLoader(\n", - " Subset(dataset, val_idx),\n", - " batch_size=BATCH_SIZE,\n", - " shuffle=False,\n", - " collate_fn=collate_fn\n", - " )\n", - " mAP50 = evaluate_map(model, val_loader, device, coco_gt)\n", - "\n", - " sizes.append(len(train_idx))\n", - " perf.append(mAP50)\n", - "\n", - " if not pool_idx:\n", - " break\n", - " if strategy == 'random':\n", - " add = random.sample(pool_idx, min(POOL_BATCH, len(pool_idx)))\n", - " else:\n", - " add = select_uncertain(pool_idx, dataset, model, device,\n", - " min(POOL_BATCH, len(pool_idx)))\n", - " train_idx += add\n", - " pool_idx = [i for i in pool_idx if i not in add]\n", - "\n", - " return sizes, perf\n", - "\n", - "if __name__ == \"__main__\":\n", - " df = parse_coco(TRAIN_JSON, TRAIN_DIR)\n", - " mapping = {orig: i+1 for i, orig in enumerate(df['label_orig'].unique())}\n", - " df['label'] = df['label_orig'].map(mapping)\n", - " df[['image_path','xmin','ymin','xmax','ymax','label']].to_csv(CSV_FILE, index=False)\n", - " coco_gt = build_coco_gt(df, GT_JSON)\n", - " dataset = DaisyDataset(CSV_FILE, TRAIN_DIR)\n", - " n_classes = df['label'].nunique() + 1 # + background\n", - "\n", - " results = {}\n", - " for strat in SAMPLINGS:\n", - " sizes, perf = run_rounds(strat, dataset, n_classes, coco_gt, DEVICE)\n", - " results[strat] = (sizes, perf)\n", - "\n", - " # plot both curves\n", - " plt.plot(*results['random'], marker='o', label='Random sampling')\n", - " plt.plot(*results['uncertainty'], marker='s', label='Uncertainty sampling')\n", - " plt.xlabel(\"# Training Images\")\n", - " plt.ylabel(\"mAP@0.5\")\n", - " plt.title(\"Daisy Active Learning: Random vs Uncertainty\")\n", - " plt.legend()\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db5ce57b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/deepforest/data/flowers/ground_truth.json b/src/deepforest/data/flowers/ground_truth.json 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--git a/tests/test_active_learning.py b/tests/test_active_learning.py new file mode 100644 index 000000000..c09dd1cce --- /dev/null +++ b/tests/test_active_learning.py @@ -0,0 +1,262 @@ +import math +from pathlib import Path +import pandas as pd +import numpy as np +import pytest + +from deepforest import active_learning as al + + +@pytest.fixture +def tmp_paths(tmp_path): + workdir = tmp_path / "work" + images_dir = tmp_path / "images" + workdir.mkdir() + images_dir.mkdir() + return workdir, images_dir + + +@pytest.fixture +def cfg(tmp_paths): + workdir, images_dir = tmp_paths + train_csv = workdir / "train.csv" + val_csv = workdir / "val.csv" + # Minimal DF-format CSVs + df = pd.DataFrame([ + {"image_path": str(images_dir / "a.jpg"), "xmin": 0, "ymin": 0, "xmax": 10, "ymax": 10, "label": "tree"}, + ]) + df.to_csv(train_csv, index=False) + df.to_csv(val_csv, index=False) + return al.Config( + workdir=str(workdir), + images_dir=str(images_dir), + train_csv=str(train_csv), + val_csv=str(val_csv), + classes=["tree", "snag"], + epochs_per_round=1, + batch_size=1, + precision=32, + device="cpu", + k_per_round=2, + score_threshold_pred=0.2, + ) + + +class DummyModel: + def __init__(self, batch_size=1, num_classes=2, predict_returns=None): + self.config = {"train": {}, "val": {}, "batch_size": batch_size, "num_classes": num_classes} + self._predict_returns = predict_returns or {} + self.trainer = None + + def use_release(self): + pass + + def create_trainer(self, trainer): + self.trainer = trainer + + def eval(self): + pass + + def predict_image(self, image_path, return_plot=False, score_threshold=0.2): + # Return a small DF or None based on path + ret = self._predict_returns.get(image_path) + if ret is None: + return None + return pd.DataFrame(ret) + + def evaluate(self, csv_file, root_dir, iou_threshold, predictions=None): + # Return a dict like DF evaluate usually does + return {"val_map": 0.42, "iou_threshold": iou_threshold} + + +class DummyTrainer: + def __init__(self, **kwargs): + self.kwargs = kwargs + + def fit(self, model): + # Simulate a training loop having run + pass + + +class DummyCheckpoint: + def __init__(self, dirpath, filename, monitor, mode, save_top_k, save_weights_only, auto_insert_metric_name): + self.dirpath = dirpath + self.best_model_path = str(Path(dirpath) / "best.ckpt") + self.monitor = monitor + self.mode = mode + + +class DummyEarlyStopping: + def __init__(self, monitor, mode, patience): + self.monitor = monitor + self.mode = mode + self.patience = patience + + +@pytest.fixture(autouse=True) +def patch_lightning_and_df(monkeypatch): + # Patch deepforest.main.deepforest factory to return DummyModel + from deepforest import main as df_main + + def factory(): + return DummyModel() + + monkeypatch.setattr(df_main, "deepforest", factory, raising=True) + + # Patch PL Trainer and callbacks used by the module + import pytorch_lightning as pl + monkeypatch.setattr(pl, "Trainer", DummyTrainer, raising=True) + + from pytorch_lightning import callbacks as cb + monkeypatch.setattr(cb, "ModelCheckpoint", DummyCheckpoint, raising=True) + monkeypatch.setattr(cb, "EarlyStopping", DummyEarlyStopping, raising=True) + + # Also patch top-level imports used inside the module + monkeypatch.setattr(al, "ModelCheckpoint", DummyCheckpoint, raising=True) + monkeypatch.setattr(al, "EarlyStopping", DummyEarlyStopping, raising=True) + + +def test_config_fields_round_trip(cfg): + assert cfg.classes == ["tree", "snag"] + assert cfg.epochs_per_round == 1 + assert cfg.k_per_round == 2 + assert cfg.iou_eval == 0.5 + + +def test_seed_function_does_not_error(): + # Should be silent even without CUDA + al._seed_everything(123) + + +def test_resolve_device_cpu_when_no_cuda(monkeypatch): + # Simulate no CUDA + monkeypatch.setattr(al.torch, "cuda", type("cuda", (), {"is_available": staticmethod(lambda: False)})) + accelerator, devices = al._resolve_device("auto") + assert accelerator == "cpu" + assert devices == 1 + + +def test_read_paths_file_from_list(tmp_paths): + _, images_dir = tmp_paths + p1 = str(images_dir / "u1.jpg") + p2 = str(images_dir / "u2.jpg") + out = al._read_paths_file([p1, p2]) + assert out == [p1, p2] + + +def test_read_paths_file_from_text(tmp_path): + f = tmp_path / "paths.txt" + f.write_text("a.jpg\n\n# comment\nb.jpg\n", encoding="utf-8") + # Current helper doesn't strip comments, but should ignore blanks + out = al._read_paths_file(f) + assert out == ["a.jpg", "# comment", "b.jpg"] + + +def test_entropy_empty_preds_returns_logC(): + entropy, n, mean = al._image_entropy_from_predictions(pd.DataFrame(), classes=["a", "b", "c"]) + assert pytest.approx(entropy, rel=1e-6) == math.log(3) + assert n == 0 + assert mean == 0.0 + + +def test_entropy_simple_distribution(): + df = pd.DataFrame( + [ + {"label": "a", "score": 0.8}, + {"label": "a", "score": 0.2}, + {"label": "b", "score": 1.0}, + ] + ) + entropy, n, mean = al._image_entropy_from_predictions(df, classes=["a", "b", "c"]) + # Mass: a=1.0, b=1.0, c=0.0 => probs=[0.5,0.5,0.0] + assert pytest.approx(entropy, rel=1e-6) == -2 * (0.5 * math.log(0.5)) + assert n == 3 + assert pytest.approx(mean, rel=1e-6) == np.mean([0.8, 0.2, 1.0]) + + +def test_active_learner_initializes_and_attaches_data(cfg): + learner = al.ActiveLearner(cfg) + # Data is attached into model.config + t = learner.model.config["train"] + v = learner.model.config["val"] + assert Path(t["csv_file"]).name == Path(cfg.train_csv).name + assert Path(v["csv_file"]).name == Path(cfg.val_csv).name + assert Path(t["root_dir"]).name == Path(cfg.images_dir).name + + +def test_fit_one_round_returns_checkpoint_path(cfg): + learner = al.ActiveLearner(cfg) + ckpt = learner.fit_one_round() + assert str(ckpt).endswith("best.ckpt") + assert Path(ckpt).parent.name == "checkpoints" + + +def test_evaluate_returns_metrics(cfg): + learner = al.ActiveLearner(cfg) + metrics = learner.evaluate() + assert "val_map" in metrics + assert metrics["iou_threshold"] == cfg.iou_eval + + +def test_predict_images_handles_none_returns(cfg, tmp_paths, monkeypatch): + workdir, images_dir = tmp_paths + p1 = str(images_dir / "x.jpg") + p2 = str(images_dir / "y.jpg") + + # Patch the DummyModel to return None for one path and a small DF for the other + dm = DummyModel(predict_returns={ + p1: [{"label": "tree", "score": 0.7, "xmin": 0, "ymin": 0, "xmax": 1, "ymax": 1}], + # p2 -> None (implicit) + }) + + def factory(): + return dm + + from deepforest import main as df_main + monkeypatch.setattr(df_main, "deepforest", factory, raising=True) + + learner = al.ActiveLearner(cfg) + out = learner.predict_images([p1, p2]) + assert set(out.keys()) == {p1, p2} + assert len(out[p1]) == 1 + # For None, code should create an empty DF with expected columns + assert list(out[p2].columns) == ["xmin", "ymin", "xmax", "ymax", "label", "score", "image_path"] + assert len(out[p2]) == 0 + + +def test_select_for_labeling_writes_manifest_and_returns_topk(cfg, tmp_paths, monkeypatch): + _, images_dir = tmp_paths + u1 = str(images_dir / "u1.jpg") + u2 = str(images_dir / "u2.jpg") + u3 = str(images_dir / "u3.jpg") + + # Create a paths file + paths_file = Path(cfg.workdir) / "unlabeled.txt" + paths_file.write_text(f"{u1}\n{u2}\n{u3}\n", encoding="utf-8") + + # Build predictable predictions so entropy differs + # u1: balanced mass across 2 classes -> higher entropy + # u2: single-class mass -> lower entropy + # u3: no preds -> max entropy (log C) + dm = DummyModel(predict_returns={ + u1: [{"label": "tree", "score": 0.5}, {"label": "snag", "score": 0.5}], + u2: [{"label": "tree", "score": 1.0}], + # u3 -> None + }) + + def factory(): + return dm + + from deepforest import main as df_main + monkeypatch.setattr(df_main, "deepforest", factory, raising=True) + + learner = al.ActiveLearner(cfg) + topk = learner.select_for_labeling(paths_file, k=2) + + # Manifest file exists + manifest_path = Path(cfg.workdir) / "acquisition" / "selection_round.csv" + assert manifest_path.exists() + + # Top-2 should include u3 (empty -> max entropy) and u1 (balanced) + selected = set(topk["image_path"].tolist()) + assert {u3, u1}.issubset(selected) From b15973d6165583decb376b549cccd2df6368dc55 Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Tue, 26 Aug 2025 23:12:33 +0530 Subject: [PATCH 3/8] add docs --- docs/user_guide/17_active_learning.md | 364 ++++++++++++++++++++++++++ src/deepforest/label_studio.py | 0 2 files changed, 364 insertions(+) create mode 100644 docs/user_guide/17_active_learning.md create mode 100644 src/deepforest/label_studio.py diff --git a/docs/user_guide/17_active_learning.md b/docs/user_guide/17_active_learning.md new file mode 100644 index 000000000..b3781f1b0 --- /dev/null +++ b/docs/user_guide/17_active_learning.md @@ -0,0 +1,364 @@ +# Overview + +Active learning in the context of image object detection is a technique for efficiently selecting the most valuable images and objects to annotate, reducing the labeling effort required for model training while maximizing performance gains. This submodule provides active learning utilities. It wraps DeepForest’s training and inference APIs with a small, reproducible loop that selects informative unlabeled images using an entropy-based acquisition function. It is designed to be extensible, so you can swap selection strategies, wire it to an annotation tool (Label Studio), and run multi-round training. + +# Key Features + +* Reproducible training rounds with seeds and configurable hardware +* Minimal configuration via a `Config` dataclass +* Lightning-based training with checkpointing and early stopping +* Batch prediction over image lists +* Entropy-based acquisition that scores each image by uncertainty +* CSV- and image-path–based I/O fully compatible with DeepForest +* Ready-to-wire hooks for Label Studio integration (pre-annotations, export/import) + +# Directory Layout + +* `workdir/` + + * `logs/` Lightning logs and checkpoints + * `acquisition/` acquisition manifests (`selection_round.csv`) +* `images_dir/` all image files accessible by relative `image_path` +* `train.csv`, `val.csv` DeepForest-format CSVs with columns: `image_path,xmin,ymin,xmax,ymax,label` + +# Quickstart + +```python +from active_learning import Config, ActiveLearner + +cfg = Config( + workdir="/tmp/df_active", + images_dir="/data/trees/images", + train_csv="/data/trees/train.csv", + val_csv="/data/trees/val.csv", + classes=["Tree"], + epochs_per_round=10, + batch_size=4, + precision=32, + device="auto", + k_per_round=50, +) + +al = ActiveLearner(cfg) +ckpt = al.fit_one_round() # train for one round +metrics = al.evaluate() # evaluate on val set + +# Select 50 uncertain images from a list or a file of paths +manifest = al.select_for_labeling("/data/trees/unlabeled_paths.txt") +print(manifest.head()) +``` + +# How It Works + +1. **Training**: `ActiveLearner.fit_one_round()` creates a PL Trainer with checkpointing and early stopping, then trains a DeepForest model for `epochs_per_round`. +2. **Evaluation**: `ActiveLearner.evaluate()` runs DeepForest’s evaluation over `val_csv` with the configured IoU threshold. +3. **Prediction**: `ActiveLearner.predict_images(paths)` runs `model.predict_image` on each path and returns a dict of DataFrames. +4. **Acquisition**: `select_for_labeling()` aggregates per-image predictions into a class-score distribution, computes Shannon entropy, and returns a ranked DataFrame (highest entropy first). It also saves `acquisition/selection_round.csv` for traceability. + +# Configuration Reference + +`Config` dataclass fields and effects: + +* **workdir**: Root for logs, checkpoints, acquisition manifests +* **images\_dir**: Base directory for all images referenced by CSVs +* **train\_csv, val\_csv**: DeepForest-format CSVs +* **classes**: List of class labels; sets `model.config["num_classes"]` +* **epochs\_per\_round**: Max epochs per training round +* **batch\_size**: Sets `model.config["batch_size"]` +* **lr, weight\_decay**: Optimizer hyperparameters (reserved for future use if you extend the Trainer/model init) +* **precision**: PL precision (e.g., 16/32/"bf16") +* **device**: `"auto"`, `"cpu"`, or `"cuda:0"` etc. Resolved to PL `accelerator`/`devices` +* **num\_workers**: Dataloader workers (if wired into DataModules in extensions) +* **seed**: Seeds Python/NumPy/Torch and calls `pl.seed_everything` +* **use\_release\_weights**: If `True`, calls `model.use_release()` to warm start +* **iou\_eval**: IoU threshold used by `evaluate()` +* **k\_per\_round**: Default number of images to acquire +* **score\_threshold\_pred**: Score threshold for predictions during acquisition + +# API Reference + +## Utility Functions + +* `_seed_everything(seed)` + + * Seeds Python, NumPy, Torch; tries `pl.seed_everything(..., workers=True)`. + +* `_resolve_device(device)` + + * Returns `(accelerator, devices)` for PL Trainer. Supports "auto", explicit CUDA, and CPU fallback. + +* `_ensure_dir(pathlike)` + + * Creates the directory path if it doesn’t exist. + +* `_read_paths_file(path_or_list)` + + * Accepts a list/tuple of paths, or a text file containing newline-separated paths. Returns a list of string paths. + +* `_image_entropy_from_predictions(pred_df, classes)` + + * Aggregates detection `score` per `label` and computes Shannon entropy over the normalized class distribution. Empty predictions are treated as maximally uncertain (`log(C)`). Returns `(entropy, n_preds, mean_score)`. + +## Class: ActiveLearner + +Constructor + +```python +ActiveLearner(cfg: Config) +``` + +* Creates work, logs, and acquisition directories +* Initializes DeepForest model with correct class count and batch size +* Optionally loads release weights +* Builds a PL Trainer with ModelCheckpoint and EarlyStopping +* Attaches train/val CSVs and roots to `model.config` + +Methods + +* `fit_one_round() -> Path` + + * Trains for `epochs_per_round`. Returns best checkpoint path. + +* `evaluate() -> dict` + + * Runs validation evaluation via DeepForest’s API. Returns a dict with metrics and optional DataFrames. + +* `predict_images(paths) -> dict[str, pd.DataFrame]` + + * Predicts on each image path. Returns mapping `path -> predictions_df`. Missing or erroring images yield empty DataFrames with proper columns. + +* `select_for_labeling(unlabeled_paths, k=None) -> pd.DataFrame` + + * Computes entropy per image from predictions, ranks descending, writes `acquisition/selection_round.csv`, and returns the top `k` rows with columns: `image_path, entropy, n_preds, mean_score`. + +# Input & Output Formats + +## DeepForest CSV (training/validation) + +Columns required: `image_path,xmin,ymin,xmax,ymax,label` + +* `image_path` can be absolute or relative to `images_dir` +* Coordinates are pixel units in the image coordinate system + +## Prediction DataFrame + +Returned by `model.predict_image` and normalized here to columns: + +* `xmin, ymin, xmax, ymax, label, score, image_path` + +# Logging, Checkpoints, and Reproducibility + +* Checkpoints saved under `workdir/logs/checkpoints/` +* Best model path tracked via `ModelCheckpoint` +* Training is deterministic where possible; seeds are set for Python, NumPy, and Torch +* Early stopping monitors `val_map` with patience 3 by default + +# Error Handling Notes + +* Prediction exceptions are caught per-image; an empty DataFrame is substituted +* Evaluation failure returns an empty dict and logs a warning +* CUDA unavailability falls back to CPU with a warning if CUDA was explicitly requested + +# Example: Multi-Round Active Learning Loop + +```python +al = ActiveLearner(cfg) +for round_id in range(5): + print(f"Round {round_id}") + ckpt = al.fit_one_round() + print(al.evaluate()) + manifest = al.select_for_labeling("/data/unlabeled_paths.txt", k=cfg.k_per_round) + # Send `manifest` to an annotation workflow (e.g., Label Studio) + # Merge newly labeled data into train.csv, deduplicate, and continue +``` + +# Label Studio Integration Plan + +This section outlines how to connect the acquisition outputs to Label Studio for annotation and then flow the results back into DeepForest. + +## Workflow + +1. **Select images** with `select_for_labeling()` to produce `selection_round.csv`. +2. **Upload images** to Label Studio using the SDK or UI. +3. **Create a project** with a bounding-box labeling config and class set derived from `cfg.classes`. +4. **(Optional) Pre-annotate** by pushing model predictions as prelabels to speed up annotation. +5. **Annotate** in Label Studio. +6. **Export annotations** as JSON. +7. **Convert** Label Studio JSON to DeepForest CSV and append to `train_csv` for the next round. + +## Labeling Config Template (Bounding Boxes) + +Replace the `choices` with your classes. + +```xml + + + + + +``` + +## Creating a Project and Uploading Tasks + +```python +from label_studio_sdk import Client + +ls = Client(url="http://localhost:8080", api_key="YOUR_API_KEY") +project = ls.start_project( + title="DeepForest Active Learning", + label_config=open("bbox_config.xml").read(), + description="AL round annotations" +) + +# Upload image paths selected for this round +import pandas as pd +sel = pd.read_csv("/tmp/df_active/acquisition/selection_round.csv") + +# Each task data can point to a local file path or a URL accessible by the server +tasks = [{"data": {"image": path}} for path in sel.image_path.tolist()] +project.import_tasks(tasks) +``` + +## Pushing Pre-Annotations (Optional) + +Use your current model to create prelabels so annotators edit rather than draw from scratch. Convert pixel boxes to Label Studio relative percentages. + +```python +import json +from PIL import Image + +results = [] +for img_path, df in al.predict_images(sel.image_path.tolist()).items(): + w, h = Image.open(img_path).size + for _, r in df.iterrows(): + x = 100 * r["xmin"] / w + y = 100 * r["ymin"] / h + width = 100 * (r["xmax"] - r["xmin"]) / w + height = 100 * (r["ymax"] - r["ymin"]) / h + results.append({ + "data": {"image": img_path}, + "predictions": [{ + "result": [{ + "from_name": "label", + "to_name": "img", + "type": "rectanglelabels", + "value": { + "x": x, "y": y, "width": width, "height": height, + "rotation": 0, "rectanglelabels": [str(r["label"])] + }, + "score": float(r.get("score", 0.0)) + }] + }] + }) + +# Bulk import with predictions +project.import_tasks(results) +``` + +## Converting Label Studio Export to DeepForest CSV + +Label Studio’s JSON export stores boxes in percentages; convert them back to pixels using the source image size. + +```python +import json +import pandas as pd +from PIL import Image +from pathlib import Path + +def labelstudio_to_deepforest(ls_export_json: str, out_csv: str): + rows = [] + data = json.load(open(ls_export_json, "r", encoding="utf-8")) + for task in data: + img_path = task["data"]["image"] + # Resolve to filesystem path if needed + p = Path(img_path) + if not p.exists(): + # Add your own mapping logic here (e.g., strip URL prefix) + continue + w, h = Image.open(p).size + for ann in task.get("annotations", []): + for res in ann.get("result", []): + if res.get("type") != "rectanglelabels": + continue + v = res["value"] + xmin = v["x"] * w / 100.0 + ymin = v["y"] * h / 100.0 + xmax = xmin + v["width"] * w / 100.0 + ymax = ymin + v["height"] * h / 100.0 + label = v["rectanglelabels"][0] + rows.append({ + "image_path": str(p), + "xmin": int(round(xmin)), + "ymin": int(round(ymin)), + "xmax": int(round(xmax)), + "ymax": int(round(ymax)), + "label": label, + }) + pd.DataFrame(rows).to_csv(out_csv, index=False) + +# Example +# labelstudio_to_deepforest("/exports/project-1-at-2025-08-25-10-00-00.json", +# "/data/trees/new_round_labels.csv") +``` + +## Automating the Round-Trip + +* After annotators finish a batch, export JSON, convert to DeepForest CSV, and append to `train_csv`. +* Deduplicate by `(image_path, xmin, ymin, xmax, ymax, label)` if necessary. +* Optionally use Label Studio webhooks to trigger a small script that runs the conversion and kicks off the next `fit_one_round()`. + +# Extending the Acquisition Strategy + +The current entropy score uses class-distribution uncertainty from detection scores. You can plug in other criteria: + +* **Score margin**: difference between top-2 class masses +* **Mean score**: prioritize low-confidence images +* **Diversity**: add image embeddings and do k-center or clustering over features +* **Spatial entropy**: weight by number of boxes or box-area variance +* **Cost-aware**: penalize large, hard-to-annotate images +* **BALD/MC-Dropout**: approximate Bayesian uncertainty via stochastic forward passes + +Tip: return a composite score and store components in the manifest for auditability. + +# Submodule Placement in DeepForest + +Recommended layout inside the DeepForest repo: + +* `deepforest/active_learning/` + + * `__init__.py` exports `Config`, `ActiveLearner` + * `acquire.py` selection utilities (entropy, margin, diversity) + * `label_studio.py` import/export helpers and SDK wiring + * `cli.py` small CLI for common workflows + * `README.md` quickstart plus examples + +Packaging notes + +* Keep Label Studio as an **optional extra**: `pip install deepforest[active]` or `pip install label-studio-sdk` +* Avoid changing `deepforest.main.deepforest` internals; interact via its public API + +# CLI Sketch + +```bash +# Train one round +python -m deepforest.active_learning.cli \ + --workdir /tmp/df_active \ + --images_dir /data/trees/images \ + --train_csv /data/trees/train.csv \ + --val_csv /data/trees/val.csv \ + --classes Tree \ + fit + +# Acquire top-K unlabeled images from a text file +python -m deepforest.active_learning.cli acquire \ + --unlabeled /data/unlabeled.txt \ + --k 50 + +# Convert Label Studio export to DeepForest CSV +python -m deepforest.active_learning.cli ls2csv \ + --export /exports/project.json \ + --out /data/new_labels.csv +``` + diff --git a/src/deepforest/label_studio.py b/src/deepforest/label_studio.py new file mode 100644 index 000000000..e69de29bb From eb3752571d2c8603c3ee0318912686a2100f3715 Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Thu, 4 Sep 2025 17:26:58 +0530 Subject: [PATCH 4/8] add label studio integration functions --- src/deepforest/active_learning.yml | 26 +++ src/deepforest/label_studio.py | 267 +++++++++++++++++++++++++++++ tests/test_label_studio.py | 0 3 files changed, 293 insertions(+) create mode 100644 src/deepforest/active_learning.yml create mode 100644 tests/test_label_studio.py diff --git a/src/deepforest/active_learning.yml b/src/deepforest/active_learning.yml new file mode 100644 index 000000000..f1e952ea2 --- /dev/null +++ b/src/deepforest/active_learning.yml @@ -0,0 +1,26 @@ +# Active learning config for DeepForest +# Fill in the required paths and class labels for your project. +workdir: ./workdir +images_dir: ./images +train_csv: ./train.csv +val_csv: ./val.csv +classes: # List of class labels + - tree + +# Training +epochs_per_round: 10 +batch_size: 4 +lr: 0.0001 +weight_decay: 0.0001 +precision: 32 # Can be 16/32 or "bf16" depending on your PL install +device: auto # "auto", "cpu", or "cuda:0" +num_workers: 4 +seed: 42 +use_release_weights: false # Warm start from NEON release weights + +# Evaluation +iou_eval: 0.5 + +# Acquisition +k_per_round: 50 +score_threshold_pred: 0.2 diff --git a/src/deepforest/label_studio.py b/src/deepforest/label_studio.py index e69de29bb..003f9353e 100644 --- a/src/deepforest/label_studio.py +++ b/src/deepforest/label_studio.py @@ -0,0 +1,267 @@ +# pip install --upgrade requests pillow + +import os +import json +import pathlib +import requests +from urllib.parse import urlparse, parse_qs +from typing import Dict, Any, List, Tuple, Optional + +BASE_URL = "http://localhost:8080" +REFRESH_TOKEN = ("your_refresh_token_here") # Replace with your actual refresh token + +TRAIN_DIR = "train_set" +os.makedirs(TRAIN_DIR, exist_ok=True) + + +def get_access_token() -> str: + """Obtain a new access token using the refresh token. + + Sends a POST request to the API's token refresh endpoint with the provided refresh token. + Raises an HTTPError if the request fails. + + Returns: + str: The newly obtained access token. + + Raises: + requests.HTTPError: If the HTTP request to refresh the token fails. + """ + r = requests.post( + f"{BASE_URL}/api/token/refresh", + json={"refresh": REFRESH_TOKEN}, + timeout=10, + headers={"Content-Type": "application/json"}, + ) + r.raise_for_status() + return r.json()["access"] + + +def Health_check(access: str) -> Dict[str, str]: + """Generate an authorization header for Label Studio API requests. + + Args: + access (str): The access token to be used for authentication. + + Returns: + Dict[str, str]: A dictionary containing the 'Authorization' header with the provided access token. + """ + return {"Authorization": f"Bearer {access}"} + + +def list_projects(access: str) -> List[Dict[str, Any]]: + """Retrieve a list of projects from the Label Studio API. + + Args: + access (str): Access token or authentication string for API requests. + + Returns: + List[Dict[str, Any]]: A list of dictionaries, each representing a project. + + Raises: + requests.HTTPError: If the HTTP request to the API fails. + """ + r = requests.get(f"{BASE_URL}/api/projects?page_size=1000000", + headers=Health_check(access), + timeout=15) + r.raise_for_status() + data = r.json() + return data["results"] if isinstance(data, dict) and "results" in data else data + + +def paginate(url: str, headers: Dict[str, str]) -> List[Dict[str, Any]]: + """Fetches and paginates results from a given API endpoint. + + Args: + url (str): The initial URL to fetch data from. + headers (Dict[str, str]): HTTP headers to include in the request. + + Returns: + List[Dict[str, Any]]: A list of items retrieved from all paginated API responses. + + Raises: + requests.HTTPError: If an HTTP error occurs during the request. + + Notes: + - Assumes the API response contains a "results" key for paginated data and a "next" key for the next page URL. + - If the response is a list or a single dictionary, it is appended directly to the results. + """ + items: List[Dict[str, Any]] = [] + next_url = url + while next_url: + r = requests.get(next_url, headers=headers, timeout=30) + r.raise_for_status() + data = r.json() + if isinstance(data, dict) and "results" in data: + items.extend(data["results"]) + next_url = data.get("next") + else: + items.extend(data if isinstance(data, list) else [data]) + next_url = None + return items + + +def list_tasks(access: str, project_id: int) -> List[Dict[str, Any]]: + """Retrieve a list of tasks from a Label Studio project, including + annotations and predictions. + + Args: + access (str): Access token or credentials for authentication. + project_id (int): The ID of the Label Studio project to fetch tasks from. + + Returns: + List[Dict[str, Any]]: A list of dictionaries, each representing a task with its associated data. + """ + # fields=all should include annotations & predictions + url = f"{BASE_URL}/api/tasks?project={project_id}&page_size=200&fields=all" + return paginate(url, Health_check(access)) + + +def get_task(access: str, task_id: int) -> Dict[str, Any]: + """Retrieve the annotation payload for a specific task from the Label + Studio API. + + Args: + access (str): Access token or authentication string for API authorization. + task_id (int): The unique identifier of the task to retrieve. + + Returns: + Dict[str, Any]: The JSON response containing the task's annotations and metadata. + + Raises: + requests.HTTPError: If the HTTP request to the API fails. + """ + # returns annotations inside the payload + url = f"{BASE_URL}/api/tasks/{task_id}/" + r = requests.get(url, headers=Health_check(access), timeout=15) + r.raise_for_status() + return r.json() + + +def export_project_tasks(access: str, project_id: int) -> List[Dict[str, Any]]: + """Export all tasks with annotations from a Label Studio project. + + Args: + access (str): Access token or authentication credential for API requests. + project_id (int): The ID of the Label Studio project to export tasks from. + + Returns: + List[Dict[str, Any]]: A list of dictionaries, each representing a task with its annotations. + + Raises: + requests.HTTPError: If the API request fails or returns an error response. + """ + # Easy Export: returns tasks with annotations + url = f"{BASE_URL}/api/projects/{project_id}/export?exportType=JSON&download_all_tasks=true" + r = requests.get(url, headers=Health_check(access), timeout=60) + r.raise_for_status() + return r.json() + + +def find_image_field(task_data: Dict[str, Any]) -> Optional[str]: + """Searches for an image file path in the provided task data dictionary. + + Iterates through the key-value pairs in `task_data` and returns the value of the first key + whose value is a string containing a common image file extension (e.g., .jpg, .png, .tiff). + + Args: + task_data (Dict[str, Any]): A dictionary containing task data, potentially including image file paths. + + Returns: + Optional[str]: The image file path if found, otherwise None. + """ + for k, v in task_data.items(): + if isinstance(v, str) and any(ext in v.lower( + ) for ext in [".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp", ".tif", ".tiff"]): + return v + return None + + +def absolute_image_url(rel_or_abs: str) -> str: + """Returns an absolute image URL. + + If the input string is already an absolute URL (starts with "http://" or "https://"), + it is returned unchanged. Otherwise, the input is treated as a relative path and + concatenated with the BASE_URL to form an absolute URL. + + Args: + rel_or_abs (str): A relative or absolute image URL. + + Returns: + str: An absolute image URL. + """ + if rel_or_abs.startswith("http://") or rel_or_abs.startswith("https://"): + return rel_or_abs + return f"{BASE_URL.rstrip('/')}/{rel_or_abs.lstrip('/')}" + + +def parse_image_url(image_url: str) -> str: + """Extracts the image filename from a given image URL. + + The function attempts to parse the filename from the URL by checking for a query parameter 'd'. + If not found, it parses the URL path and query string to extract the filename. + If extraction fails, it returns "unknown_image.jpg". + + Args: + image_url (str): The URL of the image. + + Returns: + str: The extracted image filename, or "unknown_image.jpg" if extraction fails. + """ + try: + if "?d=" in image_url: + filename = image_url.split("?d=")[-1] + else: + parsed = urlparse(image_url) + filename = pathlib.Path(parsed.path).name + if parsed.query: + qs = parse_qs(parsed.query) + if "d" in qs: + filename = qs["d"][0] + return pathlib.Path(filename.lstrip("/")).name or "unknown_image.jpg" + except Exception: + return "unknown_image.jpg" + + +def extract_annotation_pairs(task: Dict[str, Any]) -> List[Tuple[str, str]]: + """Extracts pairs of image filenames and annotation labels from a Label + Studio task. + + This function processes a Label Studio task dictionary to extract annotation pairs, + where each pair consists of the image filename and an associated label or annotation value. + It supports multiple annotation types, including choices, textarea, and any field ending + with "labels" (e.g., rectanglelabels, polygonlabels, etc.). + + Args: + task (Dict[str, Any]): A dictionary representing a Label Studio task, expected to contain + image data and annotation results. + + Returns: + List[Tuple[str, str]]: A list of tuples, each containing the image filename and a label or + annotation value extracted from the task. + """ + pairs: List[Tuple[str, str]] = [] + image_field = find_image_field(task.get("data", {}) or {}) + if not image_field: + return pairs + filename = parse_image_url(image_field) + + anns = task.get("annotations") or [] + for ann in anns: + for res in ann.get("result") or []: + t = res.get("type") + val = (res.get("value") or {}) + # choices + if t == "choices": + for c in val.get("choices", []): + pairs.append((filename, str(c))) + # textarea + if t == "textarea": + texts = val.get("text", []) + if texts: + pairs.append((filename, str(texts[0]))) + # any "*labels" list (rectanglelabels, polygonlabels, brushlabels, keypointlabels, labels, taxonomyLabels, etc.) + for key, v in val.items(): + if key.lower().endswith("labels") and isinstance(v, list): + for lab in v: + pairs.append((filename, str(lab))) + return pairs diff --git a/tests/test_label_studio.py b/tests/test_label_studio.py new file mode 100644 index 000000000..e69de29bb From f291396a120227da73512c686af05580670f1946 Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Sun, 7 Sep 2025 02:57:56 +0530 Subject: [PATCH 5/8] Improved docs and label studio helper functions --- docs/user_guide/17_active_learning.md | 407 +++++++++++--------------- docs/user_guide/AL_workflow.jpg | Bin 0 -> 213996 bytes src/deepforest/active_learning.py | 227 +++++++++----- src/deepforest/label_studio.py | 159 +++++++--- 4 files changed, 439 insertions(+), 354 deletions(-) create mode 100644 docs/user_guide/AL_workflow.jpg diff --git a/docs/user_guide/17_active_learning.md b/docs/user_guide/17_active_learning.md index b3781f1b0..57c82fc73 100644 --- a/docs/user_guide/17_active_learning.md +++ b/docs/user_guide/17_active_learning.md @@ -1,135 +1,128 @@ -# Overview +# Active Learning submodule Active learning in the context of image object detection is a technique for efficiently selecting the most valuable images and objects to annotate, reducing the labeling effort required for model training while maximizing performance gains. This submodule provides active learning utilities. It wraps DeepForest’s training and inference APIs with a small, reproducible loop that selects informative unlabeled images using an entropy-based acquisition function. It is designed to be extensible, so you can swap selection strategies, wire it to an annotation tool (Label Studio), and run multi-round training. # Key Features -* Reproducible training rounds with seeds and configurable hardware -* Minimal configuration via a `Config` dataclass -* Lightning-based training with checkpointing and early stopping -* Batch prediction over image lists -* Entropy-based acquisition that scores each image by uncertainty -* CSV- and image-path–based I/O fully compatible with DeepForest -* Ready-to-wire hooks for Label Studio integration (pre-annotations, export/import) +- Reproducible training rounds via explicit seeding +- YAML- or dict-based configuration with validation (no `Config` dataclass) +- PyTorch Lightning training with checkpointing and early stopping +- Batch prediction over image lists +- Entropy-based acquisition that scores each image by uncertainty +- DeepForest-compatible CSV and image-path I/O +- Label Studio integration `src/deepforest/label_studio.py`. Pre-annotation upload is optional and not included by default -# Directory Layout +# Basic flow of the submodule +![alt text](AL_workflow.jpg) -* `workdir/` - * `logs/` Lightning logs and checkpoints - * `acquisition/` acquisition manifests (`selection_round.csv`) -* `images_dir/` all image files accessible by relative `image_path` -* `train.csv`, `val.csv` DeepForest-format CSVs with columns: `image_path,xmin,ymin,xmax,ymax,label` # Quickstart ```python -from active_learning import Config, ActiveLearner - -cfg = Config( - workdir="/tmp/df_active", - images_dir="/data/trees/images", - train_csv="/data/trees/train.csv", - val_csv="/data/trees/val.csv", - classes=["Tree"], - epochs_per_round=10, - batch_size=4, - precision=32, - device="auto", - k_per_round=50, -) +# Recommended: configure via YAML and construct the learner with learner_from_yaml() +# Your YAML must include all required keys used by load_config() +from active_learning import learner_from_yaml -al = ActiveLearner(cfg) -ckpt = al.fit_one_round() # train for one round -metrics = al.evaluate() # evaluate on val set +learner = learner_from_yaml("active_learning.yml") + +# Train one round and evaluate +ckpt_path = learner.fit_one_round() +metrics = learner.evaluate() +print("Best checkpoint:", ckpt_path) +print("Eval metrics:", {k: v for k, v in metrics.items() if not hasattr(v, "head")}) -# Select 50 uncertain images from a list or a file of paths -manifest = al.select_for_labeling("/data/trees/unlabeled_paths.txt") +# Select uncertain images from a file of paths (one path per line) or a list +manifest = learner.select_for_labeling("/data/trees/unlabeled_paths.txt", k=50) print(manifest.head()) ``` -# How It Works - -1. **Training**: `ActiveLearner.fit_one_round()` creates a PL Trainer with checkpointing and early stopping, then trains a DeepForest model for `epochs_per_round`. -2. **Evaluation**: `ActiveLearner.evaluate()` runs DeepForest’s evaluation over `val_csv` with the configured IoU threshold. -3. **Prediction**: `ActiveLearner.predict_images(paths)` runs `model.predict_image` on each path and returns a dict of DataFrames. -4. **Acquisition**: `select_for_labeling()` aggregates per-image predictions into a class-score distribution, computes Shannon entropy, and returns a ranked DataFrame (highest entropy first). It also saves `acquisition/selection_round.csv` for traceability. - -# Configuration Reference - -`Config` dataclass fields and effects: - -* **workdir**: Root for logs, checkpoints, acquisition manifests -* **images\_dir**: Base directory for all images referenced by CSVs -* **train\_csv, val\_csv**: DeepForest-format CSVs -* **classes**: List of class labels; sets `model.config["num_classes"]` -* **epochs\_per\_round**: Max epochs per training round -* **batch\_size**: Sets `model.config["batch_size"]` -* **lr, weight\_decay**: Optimizer hyperparameters (reserved for future use if you extend the Trainer/model init) -* **precision**: PL precision (e.g., 16/32/"bf16") -* **device**: `"auto"`, `"cpu"`, or `"cuda:0"` etc. Resolved to PL `accelerator`/`devices` -* **num\_workers**: Dataloader workers (if wired into DataModules in extensions) -* **seed**: Seeds Python/NumPy/Torch and calls `pl.seed_everything` -* **use\_release\_weights**: If `True`, calls `model.use_release()` to warm start -* **iou\_eval**: IoU threshold used by `evaluate()` -* **k\_per\_round**: Default number of images to acquire -* **score\_threshold\_pred**: Score threshold for predictions during acquisition - -# API Reference - -## Utility Functions - -* `_seed_everything(seed)` +If you prefer not to use YAML, you can pass a validated dict directly to `ActiveLearner(cfg_dict)`. The dict must include the same keys that `load_config()` enforces (`workdir`, `images_dir`, `train_csv`, `val_csv`, `classes`, `training/eval/acquisition hyperparams`, etc.). - * Seeds Python, NumPy, Torch; tries `pl.seed_everything(..., workers=True)`. +`select_for_labeling()` accepts either a Python list of image paths or a text file with one path per line and ranks images by Shannon entropy of predicted class score mass. And the label Studio usage (exporting tasks and converting to DeepForest CSV) is handled by separate helper functions; they’re not part of ActiveLearner itself. -* `_resolve_device(device)` +## How It Works - * Returns `(accelerator, devices)` for PL Trainer. Supports "auto", explicit CUDA, and CPU fallback. +- **Training:** `ActiveLearner.fit_one_round()` uses the PyTorch Lightning `Trainer` that’s created in `__init__` (with checkpointing and early stopping) to train the DeepForest model for `cfg["epochs_per_round"]`. -* `_ensure_dir(pathlike)` +- **Evaluation:** `ActiveLearner.evaluate()` runs DeepForest’s evaluation on `cfg["val_csv"]` with the IoU threshold `cfg["iou_eval"]`, returning a metrics dict. - * Creates the directory path if it doesn’t exist. +- **Prediction:** `ActiveLearner.predict_images(paths)` calls `model.predict_image` on each path using `cfg["score_threshold_pred"]` and returns a dict `{image_path: DataFrame}` in DeepForest format (`xmin,ymin,xmax,ymax,label,score,image_path`). -* `_read_paths_file(path_or_list)` +- **Acquisition:** `select_for_labeling()` aggregates per-image predictions by **summing detection scores per class/label**, computes Shannon entropy (images with no predictions receive `log(C)`), sorts by entropy (desc), and returns the **top-K** rows. The **full ranked manifest** is written to `workdir/acquisition/selection_round.csv` with columns: `image_path,entropy,n_preds,mean_score`. - * Accepts a list/tuple of paths, or a text file containing newline-separated paths. Returns a list of string paths. +# Configuration -* `_image_entropy_from_predictions(pred_df, classes)` - - * Aggregates detection `score` per `label` and computes Shannon entropy over the normalized class distribution. Empty predictions are treated as maximally uncertain (`log(C)`). Returns `(entropy, n_preds, mean_score)`. +## Utility Functions +- `load_config(yaml_path)` loads a YAML with no defaults. All of these keys must be present: + +- Paths and labels +`workdir`, `images_dir`, `train_csv`, `val_csv`, `classes` (non-empty list) + +- Training hyperparameters +`epochs_per_round`, `batch_size`, `lr`, `weight_decay`, `precision`, `device`, `num_workers`, `seed`, `use_release_weights` + +- Evaluation +`iou_eval` + +- Acquisition +`k_per_round`, `score_threshold_pred` + +Example of YAML (`active_learning.yml`): +```YAML +workdir: /tmp/df_active +images_dir: /data/trees/images +train_csv: /data/trees/train.csv +val_csv: /data/trees/val.csv +classes: ["Tree"] + +epochs_per_round: 5 +batch_size: 4 +lr: 0.0005 +weight_decay: 0.0001 +precision: 32 # 16, 32, "bf16" are typical +device: auto # "auto", "cuda", or "cpu" +num_workers: 4 +seed: 1337 +use_release_weights: true + +iou_eval: 0.4 +k_per_round: 50 +score_threshold_pred: 0.05 +``` ## Class: ActiveLearner -Constructor +Construct from a YAML ```python -ActiveLearner(cfg: Config) +from active_learning import learner_from_yaml +learner = learner_from_yaml("active_learning.yml") ``` -* Creates work, logs, and acquisition directories -* Initializes DeepForest model with correct class count and batch size -* Optionally loads release weights -* Builds a PL Trainer with ModelCheckpoint and EarlyStopping -* Attaches train/val CSVs and roots to `model.config` +For reproducibility `_seed_everything(seed)` seeds Python, NumPy, Torch, and Lightning (if available) & `_resolve_device(device)` accepts "auto", "cuda...", or "cpu". By default it falls back to CPU if CUDA is unavailable. -Methods +Methods: -* `fit_one_round() -> Path` +- `fit_one_round() -> Path` +Trains for one round with checkpointing and early stopping. Returns best checkpoint path. - * Trains for `epochs_per_round`. Returns best checkpoint path. +- `evaluate() -> dict` +Evaluates on val_csv with iou_eval. Returns a dict (metrics + any DataFrame entries DeepForest provides). -* `evaluate() -> dict` +- `predict_images(paths: list[str] | str) -> dict[str, pd.DataFrame]` +Runs inference on image paths. Values are DataFrames with DeepForest columns +["xmin","ymin","xmax","ymax","label","score","image_path"]. - * Runs validation evaluation via DeepForest’s API. Returns a dict with metrics and optional DataFrames. +- `select_for_labeling(unlabeled_paths, k=None) -> pd.DataFrame` +Ranks images by Shannon entropy of predicted class score mass. -* `predict_images(paths) -> dict[str, pd.DataFrame]` + - unlabeled_paths can be a list or a text file with one path per line. - * Predicts on each image path. Returns mapping `path -> predictions_df`. Missing or erroring images yield empty DataFrames with proper columns. + - Uses k or falls back to cfg["k_per_round"]. -* `select_for_labeling(unlabeled_paths, k=None) -> pd.DataFrame` - - * Computes entropy per image from predictions, ranks descending, writes `acquisition/selection_round.csv`, and returns the top `k` rows with columns: `image_path, entropy, n_preds, mean_score`. + - Writes workdir/acquisition/selection_round.csv with columns: +image_path, entropy, n_preds, mean_score. # Input & Output Formats @@ -176,132 +169,102 @@ for round_id in range(5): This section outlines how to connect the acquisition outputs to Label Studio for annotation and then flow the results back into DeepForest. -## Workflow -1. **Select images** with `select_for_labeling()` to produce `selection_round.csv`. -2. **Upload images** to Label Studio using the SDK or UI. -3. **Create a project** with a bounding-box labeling config and class set derived from `cfg.classes`. -4. **(Optional) Pre-annotate** by pushing model predictions as prelabels to speed up annotation. -5. **Annotate** in Label Studio. -6. **Export annotations** as JSON. -7. **Convert** Label Studio JSON to DeepForest CSV and append to `train_csv` for the next round. +## 1. Helpers functions in `src/deepforest/label_studio.py` -## Labeling Config Template (Bounding Boxes) +- `get_access_token()` refreshes an access token using `REFRESH_TOKEN`. +- `Health_check(access)` returns the `Authorization` header for subsequent calls. +- `list_projects(access)` fetches Label Studio projects. +- `list_tasks(access, project_id)` retrieves tasks for a project with `fields=all` so annotations and predictions are included. +- `get_task(access, task_id)` pulls a single task and its annotations. +- `export_project_tasks(access, project_id)` downloads all tasks with annotations in JSON format. +- `find_image_field(task_data)` finds the first image-like field in a task’s `data`. +- `absolute_image_url(path_or_url)` converts relative paths to absolute URLs under `BASE_URL`. +- `parse_image_url(url)` extracts a stable filename from an image URL. +- `extract_annotation_pairs(task)` returns `(filename, label)` pairs from choices, textarea, and any `*labels` results. -Replace the `choices` with your classes. +## 2. Locate your project -```xml - - - - - +```python +projects = list_projects(access) +# pick by title or id +project = next(p for p in projects if p["title"] == "Your Project Name") +project_id = project["id"] ``` -## Creating a Project and Uploading Tasks +## 3. Fetch tasks for inspection +```python +tasks = list_tasks(access, project_id) +# or fetch a specific task +one = get_task(access, tasks[0]["id"]) +``` -```python -from label_studio_sdk import Client - -ls = Client(url="http://localhost:8080", api_key="YOUR_API_KEY") -project = ls.start_project( - title="DeepForest Active Learning", - label_config=open("bbox_config.xml").read(), - description="AL round annotations" -) - -# Upload image paths selected for this round -import pandas as pd -sel = pd.read_csv("/tmp/df_active/acquisition/selection_round.csv") - -# Each task data can point to a local file path or a URL accessible by the server -tasks = [{"data": {"image": path}} for path in sel.image_path.tolist()] -project.import_tasks(tasks) +## 4. Export annotations as JSON +```python +export = export_project_tasks(access, project_id) +# 'export' is a list of task dicts with annotations included ``` -## Pushing Pre-Annotations (Optional) +## 5. Resolve image filenames and labels +```python +pairs = [] +for task in export: + image_field = find_image_field(task.get("data", {}) or {}) + if not image_field: + continue + + img_url = absolute_image_url(image_field) + filename = parse_image_url(img_url) + + # extract_annotation_pairs already returns (filename, label) + # but we prefer the resolved 'filename' for consistency + for _, label in extract_annotation_pairs(task): + pairs.append((filename, label)) +``` -Use your current model to create prelabels so annotators edit rather than draw from scratch. Convert pixel boxes to Label Studio relative percentages. +## 6. Persist outputs for training (classification/tag use-case) ```python -import json -from PIL import Image - -results = [] -for img_path, df in al.predict_images(sel.image_path.tolist()).items(): - w, h = Image.open(img_path).size - for _, r in df.iterrows(): - x = 100 * r["xmin"] / w - y = 100 * r["ymin"] / h - width = 100 * (r["xmax"] - r["xmin"]) / w - height = 100 * (r["ymax"] - r["ymin"]) / h - results.append({ - "data": {"image": img_path}, - "predictions": [{ - "result": [{ - "from_name": "label", - "to_name": "img", - "type": "rectanglelabels", - "value": { - "x": x, "y": y, "width": width, "height": height, - "rotation": 0, "rectanglelabels": [str(r["label"])] - }, - "score": float(r.get("score", 0.0)) - }] - }] - }) - -# Bulk import with predictions -project.import_tasks(results) +import csv, os +os.makedirs(TRAIN_DIR, exist_ok=True) +out_csv = os.path.join(TRAIN_DIR, "labels.csv") + +with open(out_csv, "w", newline="") as f: + w = csv.writer(f) + w.writerow(["filename", "label"]) + w.writerows(pairs) + ``` -## Converting Label Studio Export to DeepForest CSV +## Minimum example to pull a project export: +```python +from label_studio_helpers import get_access_token, list_projects, export_project_tasks -Label Studio’s JSON export stores boxes in percentages; convert them back to pixels using the source image size. +access = get_access_token() +projects = list_projects(access) +project_id = next(p["id"] for p in projects if p["title"] == "Your Project Name") -```python -import json -import pandas as pd -from PIL import Image -from pathlib import Path - -def labelstudio_to_deepforest(ls_export_json: str, out_csv: str): - rows = [] - data = json.load(open(ls_export_json, "r", encoding="utf-8")) - for task in data: - img_path = task["data"]["image"] - # Resolve to filesystem path if needed - p = Path(img_path) - if not p.exists(): - # Add your own mapping logic here (e.g., strip URL prefix) - continue - w, h = Image.open(p).size - for ann in task.get("annotations", []): - for res in ann.get("result", []): - if res.get("type") != "rectanglelabels": - continue - v = res["value"] - xmin = v["x"] * w / 100.0 - ymin = v["y"] * h / 100.0 - xmax = xmin + v["width"] * w / 100.0 - ymax = ymin + v["height"] * h / 100.0 - label = v["rectanglelabels"][0] - rows.append({ - "image_path": str(p), - "xmin": int(round(xmin)), - "ymin": int(round(ymin)), - "xmax": int(round(xmax)), - "ymax": int(round(ymax)), - "label": label, - }) - pd.DataFrame(rows).to_csv(out_csv, index=False) - -# Example -# labelstudio_to_deepforest("/exports/project-1-at-2025-08-25-10-00-00.json", -# "/data/trees/new_round_labels.csv") +export = export_project_tasks(access, project_id) # list of task dicts ``` +## Converting Label Studio Exports + +The current helper `extract_annotation_pairs` supports classification-like outputs and returns (filename, label) pairs. + +If your Label Studio project collects bounding boxes (DeepForest use-case), you need an extra converter for rectanglelabels, because your current helpers do not compute pixel coordinates. A minimal approach is: + +**1. For each task:** + +Find the image field (`find_image_field`), resolve it (`absolute_image_url`), choose a local filename (`parse_image_url`), and ensure the corresponding image exists under `images_dir`(download if needed). + +**2. For each `annotation.result` item with `type == "rectanglelabels"`:** + +- Convert percent coords to pixels: +```python +xmin = (x/100) * W, ymin = (y/100) * H +xmax = ((x + width)/100) * W, ymax = ((y + height)/100) * H +``` +- Write a DeepForest CSV row: image_path,xmin,ymin,xmax,ymax,label. + ## Automating the Round-Trip @@ -320,45 +283,3 @@ The current entropy score uses class-distribution uncertainty from detection sco * **Cost-aware**: penalize large, hard-to-annotate images * **BALD/MC-Dropout**: approximate Bayesian uncertainty via stochastic forward passes -Tip: return a composite score and store components in the manifest for auditability. - -# Submodule Placement in DeepForest - -Recommended layout inside the DeepForest repo: - -* `deepforest/active_learning/` - - * `__init__.py` exports `Config`, `ActiveLearner` - * `acquire.py` selection utilities (entropy, margin, diversity) - * `label_studio.py` import/export helpers and SDK wiring - * `cli.py` small CLI for common workflows - * `README.md` quickstart plus examples - -Packaging notes - -* Keep Label Studio as an **optional extra**: `pip install deepforest[active]` or `pip install label-studio-sdk` -* Avoid changing `deepforest.main.deepforest` internals; interact via its public API - -# CLI Sketch - -```bash -# Train one round -python -m deepforest.active_learning.cli \ - --workdir /tmp/df_active \ - --images_dir /data/trees/images \ - --train_csv /data/trees/train.csv \ - --val_csv /data/trees/val.csv \ - --classes Tree \ - fit - -# Acquire top-K unlabeled images from a text file -python -m deepforest.active_learning.cli acquire \ - --unlabeled /data/unlabeled.txt \ - --k 50 - -# Convert Label Studio export to DeepForest CSV -python -m deepforest.active_learning.cli ls2csv \ - --export /exports/project.json \ - --out /data/new_labels.csv -``` - diff --git a/docs/user_guide/AL_workflow.jpg b/docs/user_guide/AL_workflow.jpg new file mode 100644 index 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z^RpvfyOvqa=*7jhz}w98D?akC?Z0=q{I@m{x^ZbAxyhEOShdL+gND}EX+--%S82qy z+vx-yc;y}RJHrNm(XxE!$p9?jZ3FdyqrKKg+ngUDD+{!{;yJgM7WYOi8R-M~IM~J_ zLY2`QBccm{eYGGoVO3QLJ-?Ry4%ln9jM5X*d1=HGQoF zh@YH(n4|0D+R!TGMZFg!6&l_t5t06Qk;D**-^_ya+LMHf0Q2p7HKc;4aSi!S2XFbiXEMcngJL7Y!Pj@>)~@RucUSKJCeK_yL{9 z>%K3L-KMApwhX(nh#@^o4o zC;&}v{Q%Kq|8ljy+98abY5AK#Ra*4}6j(HZ6qWnsGOns{Wwo-304Cz^Z!Y@R z>o_;3j;N5){PnW=5d=9x?J2ql{>D*6ZuaiJFooDyFSZira79lb=b dict: + """Load and validate configuration from YAML. - Attributes: - workdir: Working directory for logs, checkpoints, and acquisitions. - images_dir: Directory containing all image files. - train_csv: CSV with labeled training data (DeepForest format). - val_csv: CSV with labeled validation data. - classes: List of class labels. - - epochs_per_round: Training epochs per active learning round. - batch_size: Training batch size. - lr: Learning rate. - weight_decay: Weight decay for optimizer. - precision: Mixed precision setting (int or str, depending on PL version). - device: Device spec, e.g., "auto", "cpu", "cuda:0". - num_workers: Dataloader workers. - seed: Random seed for reproducibility. - use_release_weights: Whether to warm start from NEON release weights. - - iou_eval: IoU threshold for evaluation. - - k_per_round: Number of images to acquire per round. - score_threshold_pred: Score threshold when generating predictions. + No defaults are applied. """ - - workdir: str - images_dir: str - train_csv: str - val_csv: str - classes: list - - # Training - epochs_per_round: int = 10 - batch_size: int = 4 - lr: float = 1e-4 - weight_decay: float = 1e-4 - precision: int = 32 - device: str = "auto" - num_workers: int = 4 - seed: int = 42 - use_release_weights: bool = False - - # Evaluation - iou_eval: float = 0.5 - - # Acquisition - k_per_round: int = 50 - score_threshold_pred: float = 0.2 - - -def _seed_everything(seed): + p = Path(yaml_path) + if not p.exists(): + raise FileNotFoundError(f"Config YAML not found: {yaml_path}") + with p.open("r", encoding="utf-8") as f: + cfg = yaml.safe_load(f) or {} + + # Required keys (must all be present in YAML) + required = [ + # Paths & labels + "workdir", + "images_dir", + "train_csv", + "val_csv", + "classes", + # Training + "epochs_per_round", + "batch_size", + "lr", + "weight_decay", + "precision", + "device", + "num_workers", + "seed", + "use_release_weights", + # Evaluation + "iou_eval", + # Acquisition + "k_per_round", + "score_threshold_pred", + ] + missing = [k for k in required if k not in cfg] + if missing: + raise KeyError(f"Missing required config keys in {yaml_path}: {missing}") + + if not isinstance(cfg["classes"], (list, tuple)) or not cfg["classes"]: + raise ValueError("Config 'classes' must be a non-empty list.") + + return cfg + + +def learner_from_yaml(yaml_path: str = "active_learning.yml") -> ActiveLearner: + """Create an ActiveLearner by loading configuration from a YAML file.""" + cfg = load_config(yaml_path) + return ActiveLearner(cfg) + + +def _seed_everything(seed: int): """Seed Python, NumPy, and Torch for reproducibility.""" random.seed(seed) np.random.seed(seed) @@ -90,11 +89,12 @@ def _seed_everything(seed): pass -def _resolve_device(device): +def _resolve_device(device: str): """Return (accelerator, devices) tuple understood by PyTorch Lightning.""" - if device == "auto": + dev = str(device).lower() + if dev == "auto": return ("gpu", 1) if torch.cuda.is_available() else ("cpu", 1) - if device.startswith("cuda"): + if dev.startswith("cuda"): if not torch.cuda.is_available(): logging.warning("CUDA requested but not available; falling back to CPU.") return ("cpu", 1) @@ -163,18 +163,18 @@ class ActiveLearner: select_for_labeling(unlabeled_paths, k) -> DataFrame: Rank images by entropy. """ - def __init__(self, cfg): + def __init__(self, cfg: dict): self.cfg = cfg - self.workdir = Path(cfg.workdir) - self.images_dir = Path(cfg.images_dir) - self.train_csv = Path(cfg.train_csv) - self.val_csv = Path(cfg.val_csv) - self.classes = list(cfg.classes) + self.workdir = Path(cfg["workdir"]) + self.images_dir = Path(cfg["images_dir"]) + self.train_csv = Path(cfg["train_csv"]) + self.val_csv = Path(cfg["val_csv"]) + self.classes = list(cfg["classes"]) _ensure_dir(self.workdir) _ensure_dir(self.workdir / "logs") _ensure_dir(self.workdir / "acquisition") - _seed_everything(cfg.seed) + _seed_everything(int(cfg["seed"])) self.model = self._build_model(cfg) self.trainer, self._ckpt_cb = self._create_trainer(cfg, self.workdir / "logs") @@ -184,16 +184,18 @@ def __init__(self, cfg): def _build_model(self, cfg): """Initialize DeepForest model with correct class count.""" model = df_main.deepforest() - if cfg.use_release_weights: + if cfg["use_release_weights"]: model.use_release() - model.config["num_classes"] = len(cfg.classes) - model.config["batch_size"] = cfg.batch_size + model.config["num_classes"] = len(cfg["classes"]) + model.config["batch_size"] = int(cfg["batch_size"]) + # If desired and supported by your DeepForest version, you may also + # set optimizer hyperparameters here using cfg["lr"] / cfg["weight_decay"]. return model def _create_trainer(self, cfg, log_dir): """Create PyTorch Lightning Trainer with checkpointing and early stopping.""" - accelerator, devices = _resolve_device(cfg.device) + accelerator, devices = _resolve_device(cfg["device"]) ckpt_dir = Path(log_dir) / "checkpoints" _ensure_dir(ckpt_dir) @@ -209,10 +211,10 @@ def _create_trainer(self, cfg, log_dir): es_cb = EarlyStopping(monitor="val_map", mode="max", patience=3) trainer = pl.Trainer( - max_epochs=cfg.epochs_per_round, + max_epochs=int(cfg["epochs_per_round"]), accelerator=accelerator, devices=devices, - precision=cfg.precision, + precision=cfg["precision"], # int 16/32 or string "bf16" default_root_dir=str(log_dir), callbacks=[ckpt_cb, es_cb], deterministic=True, @@ -244,10 +246,12 @@ def fit_one_round(self): def evaluate(self): """Run evaluation on validation CSV and return results dict.""" try: - results = self.model.evaluate(csv_file=str(self.val_csv), - root_dir=str(self.images_dir), - iou_threshold=self.cfg.iou_eval, - predictions=None) + results = self.model.evaluate( + csv_file=str(self.val_csv), + root_dir=str(self.images_dir), + iou_threshold=float(self.cfg["iou_eval"]), + predictions=None, + ) log_summary = {k: v for k, v in results.items() if not hasattr(v, "head")} logging.info("Evaluation: %s", log_summary) return dict(results) @@ -267,7 +271,8 @@ def predict_images(self, paths): df = self.model.predict_image( image_path=p_str, return_plot=False, - score_threshold=self.cfg.score_threshold_pred) + score_threshold=float(self.cfg["score_threshold_pred"]), + ) if df is None: df = pd.DataFrame(columns=[ "xmin", "ymin", "xmax", "ymax", "label", "score", "image_path" @@ -285,13 +290,13 @@ def select_for_labeling(self, unlabeled_paths, k=None): Args: unlabeled_paths: List of image paths or file containing paths. - k: Number of images to return (defaults to cfg.k_per_round). + k: Number of images to return. If None, uses cfg['k_per_round']. Returns: DataFrame with ranked images and entropy scores. """ if k is None: - k = self.cfg.k_per_round + k = int(self.cfg["k_per_round"]) paths = _read_paths_file(unlabeled_paths) if not paths: @@ -307,7 +312,7 @@ def select_for_labeling(self, unlabeled_paths, k=None): "image_path": img_path, "entropy": ent, "n_preds": n_preds, - "mean_score": mean_score + "mean_score": mean_score, }) manifest = pd.DataFrame(rows).sort_values("entropy", @@ -317,3 +322,73 @@ def select_for_labeling(self, unlabeled_paths, k=None): logging.info("Wrote acquisition manifest: %s", out_path) return manifest.head(k).copy() + + def append_and_retrain(self, new_labels_csv: str, round_id=None) -> dict: + """Append new DeepForest-format labels to train_csv and retrain. + + new_labels_csv must have columns: + image_path, xmin, ymin, xmax, ymax, label + It may optionally include a 'round' column; if missing, round_id is used. + + Returns: + dict with counts and checkpoint path. + """ + new_path = Path(new_labels_csv) + if not new_path.exists(): + raise FileNotFoundError(f"New labels CSV not found: {new_labels_csv}") + + new_df = pd.read_csv(new_path) + required_cols = {"image_path", "xmin", "ymin", "xmax", "ymax", "label"} + if not required_cols.issubset(set(new_df.columns)): + raise ValueError( + f"{new_labels_csv} must contain columns {sorted(required_cols)}") + + # Ensure labels are in cfg.classes + before = len(new_df) + new_df = new_df[new_df["label"].astype(str).isin(self.classes)].copy() + filtered_out = before - len(new_df) + + # Add round column if needed + if "round" not in new_df.columns: + new_df["round"] = round_id if round_id is not None else 0 + + # Clamp to valid bounds if any stray values slipped in + def _clamp_row(r): + W = None # optional: could verify against actual image size here + r["xmin"] = max(0, int(r["xmin"])) + r["ymin"] = max(0, int(r["ymin"])) + r["xmax"] = max(int(r["xmin"]) + 1, int(r["xmax"])) + r["ymax"] = max(int(r["ymin"]) + 1, int(r["ymax"])) + return r + + new_df = new_df.apply(_clamp_row, axis=1) + + # Load existing training CSV if present + if Path(self.train_csv).exists(): + old_df = pd.read_csv(self.train_csv) + else: + old_df = pd.DataFrame(columns=list(required_cols) + ["round"]) + + # Deduplicate on exact geometry and label + key_cols = ["image_path", "xmin", "ymin", "xmax", "ymax", "label"] + merged = pd.concat([old_df, new_df], ignore_index=True) + deduped = merged.drop_duplicates(subset=key_cols, keep="first") + + added_boxes = len(deduped) - len(old_df) + added_images = deduped.tail( + added_boxes)["image_path"].nunique() if added_boxes > 0 else 0 + + deduped.to_csv(self.train_csv, index=False) + + logging.info( + "Appended labels: %d boxes (%d images). Filtered-out labels not in classes: %d. New train_csv size: %d", + added_boxes, added_images, filtered_out, len(deduped)) + + ckpt = self.fit_one_round() + return { + "added_boxes": int(added_boxes), + "added_images": int(added_images), + "filtered_out": int(filtered_out), + "checkpoint": str(ckpt), + "train_csv_size": int(len(deduped)), + } diff --git a/src/deepforest/label_studio.py b/src/deepforest/label_studio.py index 003f9353e..40569e1c0 100644 --- a/src/deepforest/label_studio.py +++ b/src/deepforest/label_studio.py @@ -1,11 +1,13 @@ -# pip install --upgrade requests pillow - import os import json +import csv import pathlib +from pathlib import Path +from typing import Dict, Any, List, Tuple, Optional, Iterable + import requests +from PIL import Image from urllib.parse import urlparse, parse_qs -from typing import Dict, Any, List, Tuple, Optional BASE_URL = "http://localhost:8080" REFRESH_TOKEN = ("your_refresh_token_here") # Replace with your actual refresh token @@ -116,45 +118,132 @@ def list_tasks(access: str, project_id: int) -> List[Dict[str, Any]]: return paginate(url, Health_check(access)) -def get_task(access: str, task_id: int) -> Dict[str, Any]: - """Retrieve the annotation payload for a specific task from the Label - Studio API. +def _filter_finished_only(tasks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Keep tasks that have at least one annotation with a non-empty result.""" + finished = [] + for t in tasks: + anns = t.get("annotations") or [] + if any((a.get("result") for a in anns)): + finished.append(t) + return finished - Args: - access (str): Access token or authentication string for API authorization. - task_id (int): The unique identifier of the task to retrieve. - Returns: - Dict[str, Any]: The JSON response containing the task's annotations and metadata. +def ls_json_to_deepforest_csv( + tasks: Iterable[Dict[str, Any]], + images_dir: str, + classes: List[str], + out_csv: str, + round_id: Optional[int] = None, + strict_labels: bool = True, +) -> Tuple[int, int]: + """Convert Label Studio rectanglelabels annotations to a DeepForest CSV. - Raises: - requests.HTTPError: If the HTTP request to the API fails. + Returns (n_boxes_written, n_unique_images_written). """ - # returns annotations inside the payload - url = f"{BASE_URL}/api/tasks/{task_id}/" - r = requests.get(url, headers=Health_check(access), timeout=15) - r.raise_for_status() - return r.json() - - -def export_project_tasks(access: str, project_id: int) -> List[Dict[str, Any]]: - """Export all tasks with annotations from a Label Studio project. - - Args: - access (str): Access token or authentication credential for API requests. - project_id (int): The ID of the Label Studio project to export tasks from. - - Returns: - List[Dict[str, Any]]: A list of dictionaries, each representing a task with its annotations. - - Raises: - requests.HTTPError: If the API request fails or returns an error response. + Path(out_csv).parent.mkdir(parents=True, exist_ok=True) + size_cache: Dict[str, Tuple[int, int]] = {} + + def _img_size(p: str) -> Optional[Tuple[int, int]]: + if p in size_cache: + return size_cache[p] + try: + with Image.open(p) as im: + size_cache[p] = im.size # (W, H) + return size_cache[p] + except Exception: + return None + + rows: List[List[Any]] = [] + images_used: set = set() + + for task in tasks: + data = task.get("data", {}) or {} + img_field = find_image_field(data) + if not img_field: + continue + + # Resolve to a filename under images_dir + fname = parse_image_url(absolute_image_url(img_field)) + img_path = str(Path(images_dir) / fname) + + size = _img_size(img_path) + if size is None: + # Image is not available locally; skip to keep dataset consistent + continue + W, H = size + + for ann in task.get("annotations") or []: + for res in ann.get("result") or []: + if res.get("type") != "rectanglelabels": + continue + val = res.get("value") or {} + labs = val.get("rectanglelabels") or [] + if not labs: + continue + label = str(labs[0]) + if strict_labels and label not in classes: + continue + + # LS percentages → pixels + try: + x = float(val.get("x", 0.0)) + y = float(val.get("y", 0.0)) + w = float(val.get("width", 0.0)) + h = float(val.get("height", 0.0)) + except Exception: + continue + + xmin = int(round((x / 100.0) * W)) + ymin = int(round((y / 100.0) * H)) + xmax = int(round(((x + w) / 100.0) * W)) + ymax = int(round(((y + h) / 100.0) * H)) + + # Clamp to image bounds + xmin = max(0, min(W, xmin)) + ymin = max(0, min(H, ymin)) + xmax = max(0, min(W, xmax)) + ymax = max(0, min(H, ymax)) + + # Require positive area + if xmax <= xmin or ymax <= ymin: + continue + + if round_id is None: + rows.append([img_path, xmin, ymin, xmax, ymax, label]) + else: + rows.append([img_path, xmin, ymin, xmax, ymax, label, round_id]) + + images_used.add(img_path) + + # Write CSV + with open(out_csv, "w", newline="") as f: + w = csv.writer(f) + header = ["image_path", "xmin", "ymin", "xmax", "ymax", "label"] + if round_id is not None: + header.append("round") + w.writerow(header) + w.writerows(rows) + + return len(rows), len(images_used) + + +def export_project_tasks(access: str, + project_id: int, + finished_only: bool = True) -> List[Dict[str, Any]]: + """Export tasks (with annotations) from a Label Studio project. + + If finished_only is True, requests the server to return only + completed tasks. """ - # Easy Export: returns tasks with annotations - url = f"{BASE_URL}/api/projects/{project_id}/export?exportType=JSON&download_all_tasks=true" + url = (f"{BASE_URL}/api/projects/{project_id}/export" + f"?exportType=JSON&download_all_tasks=true" + f"{'&onlyFinished=1' if finished_only else ''}") r = requests.get(url, headers=Health_check(access), timeout=60) r.raise_for_status() - return r.json() + data = r.json() + if finished_only and isinstance(data, list): + data = _filter_finished_only(data) + return data def find_image_field(task_data: Dict[str, Any]) -> Optional[str]: From 6d01e2b9cd6f951b452537b34fe6abfe66bb8529 Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Sun, 7 Sep 2025 16:20:26 +0530 Subject: [PATCH 6/8] Improved test_active_learning.py and fixed C/I issues --- src/deepforest/active_learning.py | 23 +- tests/test_active_learning.py | 384 +++++++++++------------------- 2 files changed, 152 insertions(+), 255 deletions(-) diff --git a/src/deepforest/active_learning.py b/src/deepforest/active_learning.py index 15daf4bb1..bfd8c3bdb 100644 --- a/src/deepforest/active_learning.py +++ b/src/deepforest/active_learning.py @@ -1,14 +1,19 @@ -"""This module provides active learning utilities for the weecology/deepforest +"""This submodule provides active learning utilities for the +weecology/deepforest library. + +Features: +- Configuration management via YAML files for active learning experiments. +- ActiveLearner class: wraps DeepForest model training, evaluation, prediction, and acquisition routines. +- Entropy-based acquisition function for selecting unlabeled images to label next. +- Utilities for reproducibility, device management, and data handling. +- Training and validation CSVs must follow DeepForest format: image_path, xmin, ymin, xmax, ymax, label. +- Supports iterative active learning workflows: model training, evaluation, prediction, selection, and retraining with new labels. + +Intended for use in tree detection and similar object detection tasks with DeepForest. library. - -It includes a Config dataclass for experiment configuration, an -ActiveLearner class that wraps DeepForest's model and training routines, -and an entropy-based acquisition function for selecting unlabeled -images. Training and validation CSV files are expected to follow the -DeepForest format, containing columns for image_path, xmin, ymin, xmax, -ymax, and label. """ +from __future__ import annotations import logging import math import random @@ -70,7 +75,7 @@ def load_config(yaml_path: str = "active_learning.yml") -> dict: return cfg -def learner_from_yaml(yaml_path: str = "active_learning.yml") -> ActiveLearner: +def learner_from_yaml(yaml_path: str = "active_learning.yml") -> "ActiveLearner": """Create an ActiveLearner by loading configuration from a YAML file.""" cfg = load_config(yaml_path) return ActiveLearner(cfg) diff --git a/tests/test_active_learning.py b/tests/test_active_learning.py index c09dd1cce..d52e512bb 100644 --- a/tests/test_active_learning.py +++ b/tests/test_active_learning.py @@ -1,262 +1,154 @@ -import math from pathlib import Path +import math +import os import pandas as pd -import numpy as np import pytest +import yaml +import numpy as np from deepforest import active_learning as al +# two standard files in src/deepforest/data/ +SRC_CSV = Path("src/deepforest/data/2018_SJER_3_252000_4107000_image_477.csv") +SRC_IMG = Path("src/deepforest/data/2018_SJER_3_252000_4107000_image_477.tif") -@pytest.fixture -def tmp_paths(tmp_path): - workdir = tmp_path / "work" - images_dir = tmp_path / "images" - workdir.mkdir() - images_dir.mkdir() - return workdir, images_dir - +pytestmark = pytest.mark.skipif( + not (SRC_CSV.exists() and SRC_IMG.exists()), + reason="Expected CSV/TIF not found" +) -@pytest.fixture -def cfg(tmp_paths): - workdir, images_dir = tmp_paths - train_csv = workdir / "train.csv" - val_csv = workdir / "val.csv" - # Minimal DF-format CSVs - df = pd.DataFrame([ - {"image_path": str(images_dir / "a.jpg"), "xmin": 0, "ymin": 0, "xmax": 10, "ymax": 10, "label": "tree"}, - ]) +def _stage_single_asset(tmp_path: Path): + """Copy CSV and image into a temp dataset and fix image_path to the copied TIF.""" + images_dir = tmp_path / "images" + images_dir.mkdir(parents=True, exist_ok=True) + # copy image + img = images_dir / SRC_IMG.name + img.write_bytes(SRC_IMG.read_bytes()) + + # rewrite CSV to point to the copied image + df = pd.read_csv(SRC_CSV) + assert "image_path" in df.columns, "CSV must include 'image_path'" + assert "label" in df.columns, "CSV must include 'label'" + df = df.copy() + df["image_path"] = str(img) # full path to the staged image + + train_csv = tmp_path / "train.csv" + val_csv = tmp_path / "val.csv" df.to_csv(train_csv, index=False) df.to_csv(val_csv, index=False) - return al.Config( - workdir=str(workdir), - images_dir=str(images_dir), - train_csv=str(train_csv), - val_csv=str(val_csv), - classes=["tree", "snag"], - epochs_per_round=1, - batch_size=1, - precision=32, - device="cpu", - k_per_round=2, - score_threshold_pred=0.2, - ) - - -class DummyModel: - def __init__(self, batch_size=1, num_classes=2, predict_returns=None): - self.config = {"train": {}, "val": {}, "batch_size": batch_size, "num_classes": num_classes} - self._predict_returns = predict_returns or {} - self.trainer = None - - def use_release(self): - pass - - def create_trainer(self, trainer): - self.trainer = trainer - - def eval(self): - pass - - def predict_image(self, image_path, return_plot=False, score_threshold=0.2): - # Return a small DF or None based on path - ret = self._predict_returns.get(image_path) - if ret is None: - return None - return pd.DataFrame(ret) - - def evaluate(self, csv_file, root_dir, iou_threshold, predictions=None): - # Return a dict like DF evaluate usually does - return {"val_map": 0.42, "iou_threshold": iou_threshold} - - -class DummyTrainer: - def __init__(self, **kwargs): - self.kwargs = kwargs - - def fit(self, model): - # Simulate a training loop having run - pass - - -class DummyCheckpoint: - def __init__(self, dirpath, filename, monitor, mode, save_top_k, save_weights_only, auto_insert_metric_name): - self.dirpath = dirpath - self.best_model_path = str(Path(dirpath) / "best.ckpt") - self.monitor = monitor - self.mode = mode - - -class DummyEarlyStopping: - def __init__(self, monitor, mode, patience): - self.monitor = monitor - self.mode = mode - self.patience = patience - - -@pytest.fixture(autouse=True) -def patch_lightning_and_df(monkeypatch): - # Patch deepforest.main.deepforest factory to return DummyModel - from deepforest import main as df_main - - def factory(): - return DummyModel() - - monkeypatch.setattr(df_main, "deepforest", factory, raising=True) - - # Patch PL Trainer and callbacks used by the module - import pytorch_lightning as pl - monkeypatch.setattr(pl, "Trainer", DummyTrainer, raising=True) - - from pytorch_lightning import callbacks as cb - monkeypatch.setattr(cb, "ModelCheckpoint", DummyCheckpoint, raising=True) - monkeypatch.setattr(cb, "EarlyStopping", DummyEarlyStopping, raising=True) - - # Also patch top-level imports used inside the module - monkeypatch.setattr(al, "ModelCheckpoint", DummyCheckpoint, raising=True) - monkeypatch.setattr(al, "EarlyStopping", DummyEarlyStopping, raising=True) - - -def test_config_fields_round_trip(cfg): - assert cfg.classes == ["tree", "snag"] - assert cfg.epochs_per_round == 1 - assert cfg.k_per_round == 2 - assert cfg.iou_eval == 0.5 - - -def test_seed_function_does_not_error(): - # Should be silent even without CUDA - al._seed_everything(123) - - -def test_resolve_device_cpu_when_no_cuda(monkeypatch): - # Simulate no CUDA - monkeypatch.setattr(al.torch, "cuda", type("cuda", (), {"is_available": staticmethod(lambda: False)})) - accelerator, devices = al._resolve_device("auto") - assert accelerator == "cpu" - assert devices == 1 - - -def test_read_paths_file_from_list(tmp_paths): - _, images_dir = tmp_paths - p1 = str(images_dir / "u1.jpg") - p2 = str(images_dir / "u2.jpg") - out = al._read_paths_file([p1, p2]) - assert out == [p1, p2] - - -def test_read_paths_file_from_text(tmp_path): - f = tmp_path / "paths.txt" - f.write_text("a.jpg\n\n# comment\nb.jpg\n", encoding="utf-8") - # Current helper doesn't strip comments, but should ignore blanks - out = al._read_paths_file(f) - assert out == ["a.jpg", "# comment", "b.jpg"] - - -def test_entropy_empty_preds_returns_logC(): - entropy, n, mean = al._image_entropy_from_predictions(pd.DataFrame(), classes=["a", "b", "c"]) - assert pytest.approx(entropy, rel=1e-6) == math.log(3) - assert n == 0 - assert mean == 0.0 + return train_csv, val_csv, images_dir, img +def _make_cfg(tmp_path: Path, train_csv: Path, val_csv: Path, images_dir: Path): + workdir = tmp_path / "work" + workdir.mkdir(parents=True, exist_ok=True) + return { + "workdir": str(workdir), + "images_dir": str(images_dir), + "train_csv": str(train_csv), + "val_csv": str(val_csv), + # CSV has a single class '0' + "classes": ["0"], + "epochs_per_round": 1, + "batch_size": 1, + "lr": 1e-3, + "weight_decay": 0.0, + "precision": 32, + "device": "cpu", + "num_workers": 0, + "seed": 123, + "use_release_weights": False, # keep offline & fast by default + "iou_eval": 0.5, + "k_per_round": 1, + "score_threshold_pred": 0.2, + } + +def test_predict_and_acquisition_with_single_image(tmp_path): + train_csv, val_csv, images_dir, img = _stage_single_asset(tmp_path) + cfg = _make_cfg(tmp_path, train_csv, val_csv, images_dir) + learner = al.ActiveLearner(cfg) -def test_entropy_simple_distribution(): + # Predict on the single image + preds = learner.predict_images([str(img)]) + assert set(preds.keys()) == {str(img)} + df = preds[str(img)] + # Even if model returns nothing, we expect these columns + assert list(df.columns) == ["xmin", "ymin", "xmax", "ymax", "label", "score", "image_path"] + + # Acquisition over the single image + unlabeled = tmp_path / "unlabeled.txt" + unlabeled.write_text(str(img) + "\n", encoding="utf-8") + topk = learner.select_for_labeling(unlabeled, k=1) + + # Manifest exists with expected schema and bounded entropy + manifest = Path(cfg["workdir"]) / "acquisition" / "selection_round.csv" + assert manifest.exists() + mdf = pd.read_csv(manifest) + for col in ["image_path", "entropy", "n_preds", "mean_score"]: + assert col in mdf.columns + + # With one class, entropy ∈ [0, ln(1)=0] so it must be 0 + assert pytest.approx(float(mdf["entropy"].iloc[0])) == 0.0 + assert len(topk) == 1 + assert topk["image_path"].iloc[0] == str(img) + + +def test_load_config_validates_and_loads(tmp_path): + # Happy path: write a minimal valid YAML from your helper cfg + train_csv, val_csv, images_dir, _ = _stage_single_asset(tmp_path) + cfg = _make_cfg(tmp_path, train_csv, val_csv, images_dir) + + yml = tmp_path / "cfg.yml" + yml.write_text(yaml.safe_dump(cfg), encoding="utf-8") + + loaded = al.load_config(str(yml)) + # Ensure all required keys survived and a couple of core values match + for k in [ + "workdir", "images_dir", "train_csv", "val_csv", "classes", + "epochs_per_round", "batch_size", "precision", "device", + "iou_eval", "k_per_round", "score_threshold_pred" + ]: + assert k in loaded + assert loaded["classes"] == ["0"] + assert int(loaded["epochs_per_round"]) == 1 + + # Missing required keys -> KeyError + bad_yml = tmp_path / "bad.yml" + bad_cfg = {k: v for k, v in cfg.items() if k not in {"classes", "workdir"}} + bad_yml.write_text(yaml.safe_dump(bad_cfg), encoding="utf-8") + with pytest.raises(KeyError): + al.load_config(str(bad_yml)) + + # Invalid classes -> ValueError + empty_classes_yml = tmp_path / "empty_classes.yml" + bad_cfg2 = cfg.copy() + bad_cfg2["classes"] = [] + empty_classes_yml.write_text(yaml.safe_dump(bad_cfg2), encoding="utf-8") + with pytest.raises(ValueError): + al.load_config(str(empty_classes_yml)) + + +def test_image_entropy_from_predictions_multiclass_and_empty(): + # Two classes; score mass: class "0" gets 1.0, class "1" gets 0.5 + classes = ["0", "1"] df = pd.DataFrame( [ - {"label": "a", "score": 0.8}, - {"label": "a", "score": 0.2}, - {"label": "b", "score": 1.0}, + {"label": "0", "score": 0.2}, + {"label": "0", "score": 0.8}, + {"label": "1", "score": 0.5}, ] ) - entropy, n, mean = al._image_entropy_from_predictions(df, classes=["a", "b", "c"]) - # Mass: a=1.0, b=1.0, c=0.0 => probs=[0.5,0.5,0.0] - assert pytest.approx(entropy, rel=1e-6) == -2 * (0.5 * math.log(0.5)) - assert n == 3 - assert pytest.approx(mean, rel=1e-6) == np.mean([0.8, 0.2, 1.0]) - - -def test_active_learner_initializes_and_attaches_data(cfg): - learner = al.ActiveLearner(cfg) - # Data is attached into model.config - t = learner.model.config["train"] - v = learner.model.config["val"] - assert Path(t["csv_file"]).name == Path(cfg.train_csv).name - assert Path(v["csv_file"]).name == Path(cfg.val_csv).name - assert Path(t["root_dir"]).name == Path(cfg.images_dir).name - - -def test_fit_one_round_returns_checkpoint_path(cfg): - learner = al.ActiveLearner(cfg) - ckpt = learner.fit_one_round() - assert str(ckpt).endswith("best.ckpt") - assert Path(ckpt).parent.name == "checkpoints" - - -def test_evaluate_returns_metrics(cfg): - learner = al.ActiveLearner(cfg) - metrics = learner.evaluate() - assert "val_map" in metrics - assert metrics["iou_threshold"] == cfg.iou_eval - - -def test_predict_images_handles_none_returns(cfg, tmp_paths, monkeypatch): - workdir, images_dir = tmp_paths - p1 = str(images_dir / "x.jpg") - p2 = str(images_dir / "y.jpg") - - # Patch the DummyModel to return None for one path and a small DF for the other - dm = DummyModel(predict_returns={ - p1: [{"label": "tree", "score": 0.7, "xmin": 0, "ymin": 0, "xmax": 1, "ymax": 1}], - # p2 -> None (implicit) - }) - - def factory(): - return dm - - from deepforest import main as df_main - monkeypatch.setattr(df_main, "deepforest", factory, raising=True) - - learner = al.ActiveLearner(cfg) - out = learner.predict_images([p1, p2]) - assert set(out.keys()) == {p1, p2} - assert len(out[p1]) == 1 - # For None, code should create an empty DF with expected columns - assert list(out[p2].columns) == ["xmin", "ymin", "xmax", "ymax", "label", "score", "image_path"] - assert len(out[p2]) == 0 - - -def test_select_for_labeling_writes_manifest_and_returns_topk(cfg, tmp_paths, monkeypatch): - _, images_dir = tmp_paths - u1 = str(images_dir / "u1.jpg") - u2 = str(images_dir / "u2.jpg") - u3 = str(images_dir / "u3.jpg") - - # Create a paths file - paths_file = Path(cfg.workdir) / "unlabeled.txt" - paths_file.write_text(f"{u1}\n{u2}\n{u3}\n", encoding="utf-8") - - # Build predictable predictions so entropy differs - # u1: balanced mass across 2 classes -> higher entropy - # u2: single-class mass -> lower entropy - # u3: no preds -> max entropy (log C) - dm = DummyModel(predict_returns={ - u1: [{"label": "tree", "score": 0.5}, {"label": "snag", "score": 0.5}], - u2: [{"label": "tree", "score": 1.0}], - # u3 -> None - }) - - def factory(): - return dm - - from deepforest import main as df_main - monkeypatch.setattr(df_main, "deepforest", factory, raising=True) - - learner = al.ActiveLearner(cfg) - topk = learner.select_for_labeling(paths_file, k=2) - - # Manifest file exists - manifest_path = Path(cfg.workdir) / "acquisition" / "selection_round.csv" - assert manifest_path.exists() - - # Top-2 should include u3 (empty -> max entropy) and u1 (balanced) - selected = set(topk["image_path"].tolist()) - assert {u3, u1}.issubset(selected) + entropy, n_preds, mean_score = al._image_entropy_from_predictions(df, classes) + + # Expected probabilities: [2/3, 1/3] + p = np.array([2/3, 1/3], dtype=float) + expected_entropy = float(-(p * np.log(p)).sum()) + assert pytest.approx(entropy, rel=1e-6) == expected_entropy + assert n_preds == 3 + assert pytest.approx(mean_score, rel=1e-6) == np.mean([0.2, 0.8, 0.5]) + + # Empty predictions -> maximum uncertainty log(C) + empty_df = pd.DataFrame(columns=["label", "score"]) + entropy2, n_preds2, mean_score2 = al._image_entropy_from_predictions(empty_df, classes) + assert pytest.approx(entropy2, rel=1e-9) == math.log(len(classes)) + assert n_preds2 == 0 + assert mean_score2 == 0.0 From 54ffceea5f7e618a3b8b0e6ffa65e79282cf224d Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Sun, 7 Sep 2025 16:57:25 +0530 Subject: [PATCH 7/8] fix import error --- source/modules.rst | 6 ++++++ src/deepforest/active_learning.py | 28 ++++++++++++++++++++-------- 2 files changed, 26 insertions(+), 8 deletions(-) create mode 100644 source/modules.rst diff --git a/source/modules.rst b/source/modules.rst new file mode 100644 index 000000000..b41cd0bf3 --- /dev/null +++ b/source/modules.rst @@ -0,0 +1,6 @@ +new_df_al +========= + +.. toctree:: + :maxdepth: 4 + diff --git a/src/deepforest/active_learning.py b/src/deepforest/active_learning.py index bfd8c3bdb..a6476c7a9 100644 --- a/src/deepforest/active_learning.py +++ b/src/deepforest/active_learning.py @@ -18,6 +18,7 @@ import math import random from pathlib import Path +from omegaconf import open_dict import yaml import numpy as np @@ -228,14 +229,25 @@ def _create_trainer(self, cfg, log_dir): ) return trainer, ckpt_cb - def _attach_training_data(self): - """Attach train/val CSVs and root dirs to model config.""" - self.model.config["train"] = self.model.config.get("train", {}) - self.model.config["val"] = self.model.config.get("val", {}) - self.model.config["train"]["csv_file"] = str(self.train_csv) - self.model.config["train"]["root_dir"] = str(self.images_dir) - self.model.config["val"]["csv_file"] = str(self.val_csv) - self.model.config["val"]["root_dir"] = str(self.images_dir) + +def _attach_training_data(self): + """Attach train/val CSVs and root dirs to model config.""" + cfg = self.model.config + + with open_dict(cfg): + if "train" not in cfg: + cfg["train"] = {} + # Some versions might use "validation"; prefer "val" if absent + if "val" not in cfg and "validation" not in cfg: + cfg["val"] = {} + + train = cfg["train"] + vkey = "val" if "val" in cfg else "validation" + + train["csv_file"] = str(self.train_csv) + train["root_dir"] = str(self.images_dir) + cfg[vkey]["csv_file"] = str(self.val_csv) + cfg[vkey]["root_dir"] = str(self.images_dir) def fit_one_round(self): """Train for one active learning round and return best checkpoint From 69440bdb5d636d28814a45369457f988265ef566 Mon Sep 17 00:00:00 2001 From: Nakshatra Date: Sun, 7 Sep 2025 17:00:44 +0530 Subject: [PATCH 8/8] Revert unintended changes --- source/modules.rst | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 source/modules.rst diff --git a/source/modules.rst b/source/modules.rst deleted file mode 100644 index b41cd0bf3..000000000 --- a/source/modules.rst +++ /dev/null @@ -1,6 +0,0 @@ -new_df_al -========= - -.. toctree:: - :maxdepth: 4 -