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baseline_log.txt
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(base) C:\Users\Gading>cd downloads
(base) C:\Users\Gading\Downloads>cd final_dl
(base) C:\Users\Gading\Downloads\final_DL>python train_baseline.py
Using device: cuda
============================================================
Loading dataset...
============================================================
TinyImageNet loaded:
Training samples: 100000
Validation samples: 10000
Test samples: 10000
Number of classes: 200
============================================================
Creating baseline model for TinyImageNet...
============================================================
Loading student model: efficientnet_b0
Parameters: 4,263,748
Model parameters: 4,263,748
============================================================
Starting Baseline Training (No Distillation)
============================================================
Epoch 1 [Train]: 100%|██████████████████████| 391/391 [04:02<00:00, 1.61it/s, loss=2.6226, acc@1=40.21%, acc@5=65.97%]
Epoch 1 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:30<00:00, 1.31it/s, loss=2.0057, acc@1=51.03%, acc@5=77.38%]
Epoch 1/50 Summary:
Train Loss: 2.6226, Acc: 40.21%
Val Loss: 2.0057, Acc: 51.03%
LR: 0.001000
New best validation accuracy: 51.03%
Epoch 2 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [00:59<00:00, 6.56it/s, loss=1.6794, acc@1=57.46%, acc@5=82.58%]
Epoch 2 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.83it/s, loss=1.8501, acc@1=54.37%, acc@5=80.04%]
Epoch 2/50 Summary:
Train Loss: 1.6794, Acc: 57.46%
Val Loss: 1.8501, Acc: 54.37%
LR: 0.000999
New best validation accuracy: 54.37%
Epoch 3 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.51it/s, loss=1.3725, acc@1=63.72%, acc@5=87.01%]
Epoch 3 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.82it/s, loss=1.8811, acc@1=54.59%, acc@5=79.96%]
Epoch 3/50 Summary:
Train Loss: 1.3725, Acc: 63.72%
Val Loss: 1.8811, Acc: 54.59%
LR: 0.000996
New best validation accuracy: 54.59%
Epoch 4 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.48it/s, loss=1.1573, acc@1=69.00%, acc@5=90.03%]
Epoch 4 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.85it/s, loss=1.9104, acc@1=55.45%, acc@5=79.79%]
Epoch 4/50 Summary:
Train Loss: 1.1573, Acc: 69.00%
Val Loss: 1.9104, Acc: 55.45%
LR: 0.000991
New best validation accuracy: 55.45%
Epoch 5 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.47it/s, loss=0.9772, acc@1=73.00%, acc@5=92.48%]
Epoch 5 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.85it/s, loss=1.9452, acc@1=55.67%, acc@5=79.90%]
Epoch 5/50 Summary:
Train Loss: 0.9772, Acc: 73.00%
Val Loss: 1.9452, Acc: 55.67%
LR: 0.000984
New best validation accuracy: 55.67%
Epoch 6 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.49it/s, loss=0.8239, acc@1=76.57%, acc@5=94.47%]
Epoch 6 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.83it/s, loss=2.0359, acc@1=55.44%, acc@5=79.78%]
Epoch 6/50 Summary:
Train Loss: 0.8239, Acc: 76.57%
Val Loss: 2.0359, Acc: 55.44%
LR: 0.000976
Epoch 7 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.49it/s, loss=0.6941, acc@1=79.83%, acc@5=95.91%]
Epoch 7 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.85it/s, loss=2.0601, acc@1=55.98%, acc@5=79.54%]
Epoch 7/50 Summary:
Train Loss: 0.6941, Acc: 79.83%
Val Loss: 2.0601, Acc: 55.98%
LR: 0.000965
New best validation accuracy: 55.98%
Epoch 8 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.48it/s, loss=0.5771, acc@1=82.77%, acc@5=97.25%]
Epoch 8 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.84it/s, loss=2.1638, acc@1=55.72%, acc@5=79.63%]
Epoch 8/50 Summary:
Train Loss: 0.5771, Acc: 82.77%
Val Loss: 2.1638, Acc: 55.72%
LR: 0.000952
Epoch 9 [Train]: 100%|██████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.47it/s, loss=0.4911, acc@1=85.22%, acc@5=97.95%]
Epoch 9 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.83it/s, loss=2.2309, acc@1=55.84%, acc@5=79.26%]
Epoch 9/50 Summary:
Train Loss: 0.4911, Acc: 85.22%
Val Loss: 2.2309, Acc: 55.84%
LR: 0.000938
Epoch 10 [Train]: 100%|█████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.47it/s, loss=0.4275, acc@1=86.99%, acc@5=98.48%]
Epoch 10 [Val]: 100%|█████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.84it/s, loss=2.2923, acc@1=55.58%, acc@5=79.60%]
Epoch 10/50 Summary:
Train Loss: 0.4275, Acc: 86.99%
Val Loss: 2.2923, Acc: 55.58%
LR: 0.000922
Epoch 11 [Train]: 100%|█████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.50it/s, loss=0.3657, acc@1=88.87%, acc@5=98.90%]
Epoch 11 [Val]: 100%|█████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.84it/s, loss=2.3785, acc@1=55.44%, acc@5=78.76%]
Epoch 11/50 Summary:
Train Loss: 0.3657, Acc: 88.87%
Val Loss: 2.3785, Acc: 55.44%
LR: 0.000905
Epoch 12 [Train]: 100%|█████████████████████████████████████████████████████████| 391/391 [01:00<00:00, 6.48it/s, loss=0.3183, acc@1=90.21%, acc@5=99.15%]
Epoch 12 [Val]: 100%|█████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.84it/s, loss=2.4403, acc@1=55.45%, acc@5=78.39%]
Epoch 12/50 Summary:
Train Loss: 0.3183, Acc: 90.21%
Val Loss: 2.4403, Acc: 55.45%
LR: 0.000885
Early stopping triggered at epoch 12
Training completed in 19.62 minutes
Best validation accuracy: 55.98%
Training history saved to ./results\baseline_history_tinyimagenet.json
============================================================
Final Test Evaluation
============================================================
C:\Users\Gading\Downloads\final_DL\train_baseline.py:277: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
best_checkpoint = torch.load(
Epoch 0 [Val]: 100%|██████████████████████████████████████████████████████████████| 40/40 [00:21<00:00, 1.86it/s, loss=2.0601, acc@1=55.98%, acc@5=79.54%]
Test Results:
Loss: 2.0601
Top-1 Accuracy: 55.98%
Top-5 Accuracy: 79.54%