FlagsDetection/101.yaml at master · whitesockcat/FlagsDetection · GitHub

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MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
  NUM_CLASSES: 41
  FASTER_RCNN: True
NUM_GPUS: 1
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  # 1x schedule (note TRAIN.IMS_PER_BATCH: 1)
  BASE_LR: 0.002
  GAMMA: 0.10
  MAX_ITER: 90000
  STEPS: [0, 60000, 80000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
RESNETS:
  STRIDE_1X1: False  # default True for MSRA; False for C2 or Torch models
  TRANS_FUNC: bottleneck_transformation
  NUM_GROUPS: 64
  WIDTH_PER_GROUP: 4
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
TRAIN:
  # WEIGHTS: None
  WEIGHTS: /userhome/flags/101/train/coco_2014_train/generalized_rcnn/model_final.pkl
  DATASETS: ('coco_2014_train',)
  SCALES: (480,)
  MAX_SIZE: 640
  IMS_PER_BATCH: 1
 # BATCH_SIZE_PER_IM: 128
  #RPN_PRE_NMS_TOP_N: 200  # Per FPN level
TEST:
  WEIGHTS: /userhome/flags/101/train/coco_2014_train/generalized_rcnn/model_final.pkl
  DATASETS: ('coco_2015_test',)
  SCALE: 480
  MAX_SIZE: 640
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
  RPN_POST_NMS_TOP_N: 2000

OUTPUT_DIR: /userhome/flags/101