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  1. checkpoints/unimedclip/unimed_clip_vit_b16.pt +3 -0
  2. checkpoints/unimedclip/unimed_clip_vit_l14_large_text_encoder.pt +3 -0
  3. exp-rank-16/resnet/fewshot_100_percent/checkpoints/best_model/checkpoint.pth +3 -0
  4. exp-rank-16/resnet/fewshot_100_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  5. exp-rank-16/resnet/fewshot_100_percent/checkpoints/best_model/model.pth +3 -0
  6. exp-rank-16/resnet/fewshot_100_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  7. exp-rank-16/resnet/fewshot_100_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
  8. exp-rank-16/resnet/fewshot_100_percent/checkpoints/checkpoint_last/model.pth +3 -0
  9. exp-rank-16/resnet/fewshot_100_percent/training_20250402-140103.log +147 -0
  10. exp-rank-16/resnet/fewshot_100_percent/training_20250402-140719.log +396 -0
  11. exp-rank-16/resnet/fewshot_10_percent/checkpoints/best_model/checkpoint.pth +3 -0
  12. exp-rank-16/resnet/fewshot_10_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  13. exp-rank-16/resnet/fewshot_10_percent/checkpoints/best_model/model.pth +3 -0
  14. exp-rank-16/resnet/fewshot_10_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  15. exp-rank-16/resnet/fewshot_10_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
  16. exp-rank-16/resnet/fewshot_10_percent/checkpoints/checkpoint_last/model.pth +3 -0
  17. exp-rank-16/resnet/fewshot_10_percent/training_20250403-005625.log +164 -0
  18. exp-rank-16/resnet/fewshot_30_percent/checkpoints/best_model/checkpoint.pth +3 -0
  19. exp-rank-16/resnet/fewshot_30_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  20. exp-rank-16/resnet/fewshot_30_percent/checkpoints/best_model/model.pth +3 -0
  21. exp-rank-16/resnet/fewshot_30_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  22. exp-rank-16/resnet/fewshot_30_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
  23. exp-rank-16/resnet/fewshot_30_percent/checkpoints/checkpoint_last/model.pth +3 -0
  24. exp-rank-16/resnet/fewshot_30_percent/training_20250403-011001.log +208 -0
  25. exp-rank-16/resnet/fewshot_75_percent/checkpoints/best_model/checkpoint.pth +3 -0
  26. exp-rank-16/resnet/fewshot_75_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  27. exp-rank-16/resnet/fewshot_75_percent/checkpoints/best_model/model.pth +3 -0
  28. exp-rank-16/resnet/fewshot_75_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  29. exp-rank-16/resnet/fewshot_75_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
  30. exp-rank-16/resnet/fewshot_75_percent/checkpoints/checkpoint_last/model.pth +3 -0
  31. exp-rank-16/resnet/fewshot_75_percent/training_20250403-013013.log +332 -0
  32. exp-rank-16/vit/fewshot_100_percent/checkpoints/best_model/checkpoint.pth +3 -0
  33. exp-rank-16/vit/fewshot_100_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  34. exp-rank-16/vit/fewshot_100_percent/checkpoints/best_model/model.pth +3 -0
  35. exp-rank-16/vit/fewshot_100_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  36. exp-rank-16/vit/fewshot_100_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
  37. exp-rank-16/vit/fewshot_100_percent/checkpoints/checkpoint_last/model.pth +3 -0
  38. exp-rank-16/vit/fewshot_100_percent/training_20250403-031835.log +398 -0
  39. exp-rank-16/vit/fewshot_10_percent/checkpoints/best_model/checkpoint.pth +3 -0
  40. exp-rank-16/vit/fewshot_10_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  41. exp-rank-16/vit/fewshot_10_percent/checkpoints/best_model/model.pth +3 -0
  42. exp-rank-16/vit/fewshot_10_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  43. exp-rank-16/vit/fewshot_10_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
  44. exp-rank-16/vit/fewshot_10_percent/checkpoints/checkpoint_last/model.pth +3 -0
  45. exp-rank-16/vit/fewshot_10_percent/training_20250403-005422.log +168 -0
  46. exp-rank-16/vit/fewshot_30_percent/checkpoints/best_model/checkpoint.pth +3 -0
  47. exp-rank-16/vit/fewshot_30_percent/checkpoints/best_model/lora_weights.safetensors +3 -0
  48. exp-rank-16/vit/fewshot_30_percent/checkpoints/best_model/model.pth +3 -0
  49. exp-rank-16/vit/fewshot_30_percent/checkpoints/checkpoint_last/checkpoint.pth +3 -0
  50. exp-rank-16/vit/fewshot_30_percent/checkpoints/checkpoint_last/lora_weights.safetensors +3 -0
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+ 2025-04-02 14:01:03,540 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=None, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='resnet', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=6), gpu=6, seed=42, num_workers=4, output_dir='./outputs/exp2/resnet/fewshot_100_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
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+ 2025-04-02 14:01:03,540 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
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+ 2025-04-02 14:02:54,817 - INFO - Initializing RobustMedClip model
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+ 2025-04-02 14:03:03,707 - INFO - Starting training
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+ 2025-04-02 14:03:03,714 - INFO - Epoch 1/20
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+ 2025-04-02 14:03:07,364 - ERROR - Error in batch 0: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
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+ 2025-04-02 14:03:07,566 - ERROR - Error in batch 1: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
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+ 2025-04-02 14:03:07,662 - ERROR - Error in batch 2: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
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+ 2025-04-02 14:03:26,645 - ERROR - Error in batch 99: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
106
+ 2025-04-02 14:03:26,749 - ERROR - Error in batch 100: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
107
+ 2025-04-02 14:03:26,852 - ERROR - Error in batch 101: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
108
+ 2025-04-02 14:03:27,293 - ERROR - Error in batch 102: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
109
+ 2025-04-02 14:03:27,383 - ERROR - Error in batch 103: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
110
+ 2025-04-02 14:03:27,470 - ERROR - Error in batch 104: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
111
+ 2025-04-02 14:03:27,560 - ERROR - Error in batch 105: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
112
+ 2025-04-02 14:03:28,299 - ERROR - Error in batch 106: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
113
+ 2025-04-02 14:03:28,392 - ERROR - Error in batch 107: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
114
+ 2025-04-02 14:03:28,507 - ERROR - Error in batch 108: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
115
+ 2025-04-02 14:03:28,651 - ERROR - Error in batch 109: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
116
+ 2025-04-02 14:03:29,103 - ERROR - Error in batch 110: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
117
+ 2025-04-02 14:03:29,220 - ERROR - Error in batch 111: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
118
+ 2025-04-02 14:03:29,313 - ERROR - Error in batch 112: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
119
+ 2025-04-02 14:03:29,406 - ERROR - Error in batch 113: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
120
+ 2025-04-02 14:03:29,972 - ERROR - Error in batch 114: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
121
+ 2025-04-02 14:03:30,084 - ERROR - Error in batch 115: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
122
+ 2025-04-02 14:03:30,180 - ERROR - Error in batch 116: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
123
+ 2025-04-02 14:03:30,290 - ERROR - Error in batch 117: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
124
+ 2025-04-02 14:03:31,010 - ERROR - Error in batch 118: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
125
+ 2025-04-02 14:03:31,106 - ERROR - Error in batch 119: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
126
+ 2025-04-02 14:03:31,186 - ERROR - Error in batch 120: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
127
+ 2025-04-02 14:03:31,309 - ERROR - Error in batch 121: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
128
+ 2025-04-02 14:03:31,826 - ERROR - Error in batch 122: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
129
+ 2025-04-02 14:03:31,920 - ERROR - Error in batch 123: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
130
+ 2025-04-02 14:03:32,007 - ERROR - Error in batch 124: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
131
+ 2025-04-02 14:03:32,100 - ERROR - Error in batch 125: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
132
+ 2025-04-02 14:03:32,627 - ERROR - Error in batch 126: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
133
+ 2025-04-02 14:03:32,711 - ERROR - Error in batch 127: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
134
+ 2025-04-02 14:03:32,795 - ERROR - Error in batch 128: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
135
+ 2025-04-02 14:03:32,874 - ERROR - Error in batch 129: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
136
+ 2025-04-02 14:03:33,298 - ERROR - Error in batch 130: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
137
+ 2025-04-02 14:03:33,377 - ERROR - Error in batch 131: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
138
+ 2025-04-02 14:03:33,546 - ERROR - Error in batch 132: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
139
+ 2025-04-02 14:03:33,661 - ERROR - Error in batch 133: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
140
+ 2025-04-02 14:03:34,173 - ERROR - Error in batch 134: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
141
+ 2025-04-02 14:03:34,249 - ERROR - Error in batch 135: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
142
+ 2025-04-02 14:03:34,331 - ERROR - Error in batch 136: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
143
+ 2025-04-02 14:03:34,403 - ERROR - Error in batch 137: shape '[256, 56, 56, 64]' is invalid for input of size 55115776
144
+ 2025-04-02 14:03:34,454 - ERROR - Error in batch 138: shape '[40, 56, 56, 64]' is invalid for input of size 8611840
145
+ 2025-04-02 14:03:34,859 - INFO - Epoch 1 - Train Loss: 0.0000
146
+ 2025-04-02 14:03:42,477 - ERROR - Error during evaluation in epoch 1: shape '[1024, 56, 56, 64]' is invalid for input of size 220463104
147
+ 2025-04-02 14:03:42,478 - INFO - Skipping evaluation for this epoch
exp-rank-16/resnet/fewshot_100_percent/training_20250402-140719.log ADDED
@@ -0,0 +1,396 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-04-02 14:07:19,177 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=None, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='resnet', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=6), gpu=6, seed=42, num_workers=4, output_dir='./outputs/exp2/resnet/fewshot_100_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
2
+ 2025-04-02 14:07:19,178 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
3
+ 2025-04-02 14:08:19,350 - INFO - Initializing RobustMedClip model
4
+ 2025-04-02 14:08:26,804 - INFO - Starting training
5
+ 2025-04-02 14:08:26,804 - INFO - Epoch 1/20
6
+ 2025-04-02 14:08:31,497 - INFO - Train Epoch: 1 [0/139 (0%)] Loss: 4.5219
7
+ 2025-04-02 14:08:39,343 - INFO - Train Epoch: 1 [10/139 (7%)] Loss: 4.3552
8
+ 2025-04-02 14:08:47,275 - INFO - Train Epoch: 1 [20/139 (14%)] Loss: 4.3738
9
+ 2025-04-02 14:08:55,259 - INFO - Train Epoch: 1 [30/139 (22%)] Loss: 4.2367
10
+ 2025-04-02 14:09:03,042 - INFO - Train Epoch: 1 [40/139 (29%)] Loss: 4.1578
11
+ 2025-04-02 14:09:10,988 - INFO - Train Epoch: 1 [50/139 (36%)] Loss: 3.9022
12
+ 2025-04-02 14:09:18,920 - INFO - Train Epoch: 1 [60/139 (43%)] Loss: 3.9384
13
+ 2025-04-02 14:09:26,740 - INFO - Train Epoch: 1 [70/139 (50%)] Loss: 3.9649
14
+ 2025-04-02 14:09:34,752 - INFO - Train Epoch: 1 [80/139 (58%)] Loss: 3.9027
15
+ 2025-04-02 14:09:42,769 - INFO - Train Epoch: 1 [90/139 (65%)] Loss: 3.8656
16
+ 2025-04-02 14:09:50,911 - INFO - Train Epoch: 1 [100/139 (72%)] Loss: 3.8772
17
+ 2025-04-02 14:09:58,825 - INFO - Train Epoch: 1 [110/139 (79%)] Loss: 3.8813
18
+ 2025-04-02 14:10:06,646 - INFO - Train Epoch: 1 [120/139 (86%)] Loss: 3.7529
19
+ 2025-04-02 14:10:14,488 - INFO - Train Epoch: 1 [130/139 (94%)] Loss: 3.7497
20
+ 2025-04-02 14:10:21,066 - INFO - Epoch 1 - Train Loss: 4.0231
21
+ 2025-04-02 14:10:46,829 - INFO - Epoch 1 - Validation Accuracy: 9.82%
22
+ 2025-04-02 14:10:46,830 - INFO - New best validation accuracy: 9.82%
23
+ 2025-04-02 14:10:48,016 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
24
+ 2025-04-02 14:10:49,090 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
25
+ 2025-04-02 14:10:49,090 - INFO - Epoch 2/20
26
+ 2025-04-02 14:10:51,245 - INFO - Train Epoch: 2 [0/139 (0%)] Loss: 3.7751
27
+ 2025-04-02 14:10:59,118 - INFO - Train Epoch: 2 [10/139 (7%)] Loss: 3.8226
28
+ 2025-04-02 14:11:07,061 - INFO - Train Epoch: 2 [20/139 (14%)] Loss: 3.6395
29
+ 2025-04-02 14:11:14,973 - INFO - Train Epoch: 2 [30/139 (22%)] Loss: 3.6151
30
+ 2025-04-02 14:11:22,881 - INFO - Train Epoch: 2 [40/139 (29%)] Loss: 3.5845
31
+ 2025-04-02 14:11:30,804 - INFO - Train Epoch: 2 [50/139 (36%)] Loss: 3.5294
32
+ 2025-04-02 14:11:38,842 - INFO - Train Epoch: 2 [60/139 (43%)] Loss: 3.5398
33
+ 2025-04-02 14:11:46,763 - INFO - Train Epoch: 2 [70/139 (50%)] Loss: 3.4746
34
+ 2025-04-02 14:11:54,696 - INFO - Train Epoch: 2 [80/139 (58%)] Loss: 3.5749
35
+ 2025-04-02 14:12:02,653 - INFO - Train Epoch: 2 [90/139 (65%)] Loss: 3.4164
36
+ 2025-04-02 14:12:10,649 - INFO - Train Epoch: 2 [100/139 (72%)] Loss: 3.5414
37
+ 2025-04-02 14:12:18,553 - INFO - Train Epoch: 2 [110/139 (79%)] Loss: 3.4853
38
+ 2025-04-02 14:12:26,574 - INFO - Train Epoch: 2 [120/139 (86%)] Loss: 3.4341
39
+ 2025-04-02 14:12:34,505 - INFO - Train Epoch: 2 [130/139 (94%)] Loss: 3.4379
40
+ 2025-04-02 14:12:40,319 - INFO - Epoch 2 - Train Loss: 3.5353
41
+ 2025-04-02 14:13:05,209 - INFO - Epoch 2 - Validation Accuracy: 21.44%
42
+ 2025-04-02 14:13:05,210 - INFO - New best validation accuracy: 21.44%
43
+ 2025-04-02 14:13:06,652 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
44
+ 2025-04-02 14:13:08,090 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
45
+ 2025-04-02 14:13:08,090 - INFO - Epoch 3/20
46
+ 2025-04-02 14:13:10,400 - INFO - Train Epoch: 3 [0/139 (0%)] Loss: 3.3996
47
+ 2025-04-02 14:13:18,340 - INFO - Train Epoch: 3 [10/139 (7%)] Loss: 3.3734
48
+ 2025-04-02 14:13:26,104 - INFO - Train Epoch: 3 [20/139 (14%)] Loss: 3.2506
49
+ 2025-04-02 14:13:33,974 - INFO - Train Epoch: 3 [30/139 (22%)] Loss: 3.3017
50
+ 2025-04-02 14:13:41,905 - INFO - Train Epoch: 3 [40/139 (29%)] Loss: 3.1492
51
+ 2025-04-02 14:13:49,857 - INFO - Train Epoch: 3 [50/139 (36%)] Loss: 3.2096
52
+ 2025-04-02 14:13:57,756 - INFO - Train Epoch: 3 [60/139 (43%)] Loss: 3.2739
53
+ 2025-04-02 14:14:05,683 - INFO - Train Epoch: 3 [70/139 (50%)] Loss: 3.3441
54
+ 2025-04-02 14:14:13,628 - INFO - Train Epoch: 3 [80/139 (58%)] Loss: 3.1666
55
+ 2025-04-02 14:14:21,538 - INFO - Train Epoch: 3 [90/139 (65%)] Loss: 3.1848
56
+ 2025-04-02 14:14:29,425 - INFO - Train Epoch: 3 [100/139 (72%)] Loss: 3.1620
57
+ 2025-04-02 14:14:37,312 - INFO - Train Epoch: 3 [110/139 (79%)] Loss: 3.2047
58
+ 2025-04-02 14:14:45,224 - INFO - Train Epoch: 3 [120/139 (86%)] Loss: 3.1099
59
+ 2025-04-02 14:14:53,154 - INFO - Train Epoch: 3 [130/139 (94%)] Loss: 3.1229
60
+ 2025-04-02 14:14:59,087 - INFO - Epoch 3 - Train Loss: 3.2229
61
+ 2025-04-02 14:15:24,308 - INFO - Epoch 3 - Validation Accuracy: 23.85%
62
+ 2025-04-02 14:15:24,309 - INFO - New best validation accuracy: 23.85%
63
+ 2025-04-02 14:15:25,884 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
64
+ 2025-04-02 14:15:27,427 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
65
+ 2025-04-02 14:15:27,427 - INFO - Epoch 4/20
66
+ 2025-04-02 14:15:29,480 - INFO - Train Epoch: 4 [0/139 (0%)] Loss: 3.0476
67
+ 2025-04-02 14:15:37,446 - INFO - Train Epoch: 4 [10/139 (7%)] Loss: 3.1307
68
+ 2025-04-02 14:15:45,516 - INFO - Train Epoch: 4 [20/139 (14%)] Loss: 3.0492
69
+ 2025-04-02 14:15:53,644 - INFO - Train Epoch: 4 [30/139 (22%)] Loss: 3.0015
70
+ 2025-04-02 14:16:01,736 - INFO - Train Epoch: 4 [40/139 (29%)] Loss: 3.0253
71
+ 2025-04-02 14:16:09,682 - INFO - Train Epoch: 4 [50/139 (36%)] Loss: 2.9705
72
+ 2025-04-02 14:16:17,814 - INFO - Train Epoch: 4 [60/139 (43%)] Loss: 2.9053
73
+ 2025-04-02 14:16:26,071 - INFO - Train Epoch: 4 [70/139 (50%)] Loss: 2.9070
74
+ 2025-04-02 14:16:34,152 - INFO - Train Epoch: 4 [80/139 (58%)] Loss: 2.9836
75
+ 2025-04-02 14:16:42,088 - INFO - Train Epoch: 4 [90/139 (65%)] Loss: 2.8902
76
+ 2025-04-02 14:16:50,075 - INFO - Train Epoch: 4 [100/139 (72%)] Loss: 2.9173
77
+ 2025-04-02 14:16:58,008 - INFO - Train Epoch: 4 [110/139 (79%)] Loss: 2.8690
78
+ 2025-04-02 14:17:06,092 - INFO - Train Epoch: 4 [120/139 (86%)] Loss: 2.9113
79
+ 2025-04-02 14:17:13,980 - INFO - Train Epoch: 4 [130/139 (94%)] Loss: 2.8097
80
+ 2025-04-02 14:17:19,803 - INFO - Epoch 4 - Train Loss: 2.9747
81
+ 2025-04-02 14:17:44,481 - INFO - Epoch 4 - Validation Accuracy: 27.90%
82
+ 2025-04-02 14:17:44,482 - INFO - New best validation accuracy: 27.90%
83
+ 2025-04-02 14:17:45,879 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
84
+ 2025-04-02 14:17:47,361 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
85
+ 2025-04-02 14:17:47,361 - INFO - Epoch 5/20
86
+ 2025-04-02 14:17:49,444 - INFO - Train Epoch: 5 [0/139 (0%)] Loss: 2.8019
87
+ 2025-04-02 14:17:57,296 - INFO - Train Epoch: 5 [10/139 (7%)] Loss: 2.8872
88
+ 2025-04-02 14:18:05,089 - INFO - Train Epoch: 5 [20/139 (14%)] Loss: 2.7613
89
+ 2025-04-02 14:18:13,036 - INFO - Train Epoch: 5 [30/139 (22%)] Loss: 2.8889
90
+ 2025-04-02 14:18:20,921 - INFO - Train Epoch: 5 [40/139 (29%)] Loss: 2.9032
91
+ 2025-04-02 14:18:28,814 - INFO - Train Epoch: 5 [50/139 (36%)] Loss: 2.8039
92
+ 2025-04-02 14:18:36,766 - INFO - Train Epoch: 5 [60/139 (43%)] Loss: 2.8803
93
+ 2025-04-02 14:18:44,655 - INFO - Train Epoch: 5 [70/139 (50%)] Loss: 2.7702
94
+ 2025-04-02 14:18:52,577 - INFO - Train Epoch: 5 [80/139 (58%)] Loss: 2.7873
95
+ 2025-04-02 14:19:00,457 - INFO - Train Epoch: 5 [90/139 (65%)] Loss: 2.7544
96
+ 2025-04-02 14:19:08,336 - INFO - Train Epoch: 5 [100/139 (72%)] Loss: 2.6804
97
+ 2025-04-02 14:19:16,275 - INFO - Train Epoch: 5 [110/139 (79%)] Loss: 2.7358
98
+ 2025-04-02 14:19:24,248 - INFO - Train Epoch: 5 [120/139 (86%)] Loss: 2.6353
99
+ 2025-04-02 14:19:32,123 - INFO - Train Epoch: 5 [130/139 (94%)] Loss: 2.6576
100
+ 2025-04-02 14:19:37,942 - INFO - Epoch 5 - Train Loss: 2.7928
101
+ 2025-04-02 14:20:02,827 - INFO - Epoch 5 - Validation Accuracy: 27.45%
102
+ 2025-04-02 14:20:04,121 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
103
+ 2025-04-02 14:20:04,121 - INFO - Epoch 6/20
104
+ 2025-04-02 14:20:05,983 - INFO - Train Epoch: 6 [0/139 (0%)] Loss: 2.7246
105
+ 2025-04-02 14:20:13,820 - INFO - Train Epoch: 6 [10/139 (7%)] Loss: 2.6457
106
+ 2025-04-02 14:20:21,745 - INFO - Train Epoch: 6 [20/139 (14%)] Loss: 2.6588
107
+ 2025-04-02 14:20:29,627 - INFO - Train Epoch: 6 [30/139 (22%)] Loss: 2.6471
108
+ 2025-04-02 14:20:37,522 - INFO - Train Epoch: 6 [40/139 (29%)] Loss: 2.6302
109
+ 2025-04-02 14:20:45,427 - INFO - Train Epoch: 6 [50/139 (36%)] Loss: 2.6366
110
+ 2025-04-02 14:20:53,308 - INFO - Train Epoch: 6 [60/139 (43%)] Loss: 2.6963
111
+ 2025-04-02 14:21:01,308 - INFO - Train Epoch: 6 [70/139 (50%)] Loss: 2.5931
112
+ 2025-04-02 14:21:09,185 - INFO - Train Epoch: 6 [80/139 (58%)] Loss: 2.6493
113
+ 2025-04-02 14:21:17,339 - INFO - Train Epoch: 6 [90/139 (65%)] Loss: 2.6008
114
+ 2025-04-02 14:21:25,325 - INFO - Train Epoch: 6 [100/139 (72%)] Loss: 2.5623
115
+ 2025-04-02 14:21:33,239 - INFO - Train Epoch: 6 [110/139 (79%)] Loss: 2.5385
116
+ 2025-04-02 14:21:41,193 - INFO - Train Epoch: 6 [120/139 (86%)] Loss: 2.6009
117
+ 2025-04-02 14:21:49,147 - INFO - Train Epoch: 6 [130/139 (94%)] Loss: 2.6881
118
+ 2025-04-02 14:21:55,137 - INFO - Epoch 6 - Train Loss: 2.6606
119
+ 2025-04-02 14:22:19,943 - INFO - Epoch 6 - Validation Accuracy: 28.41%
120
+ 2025-04-02 14:22:19,943 - INFO - New best validation accuracy: 28.41%
121
+ 2025-04-02 14:22:21,111 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
122
+ 2025-04-02 14:22:22,358 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
123
+ 2025-04-02 14:22:22,358 - INFO - Epoch 7/20
124
+ 2025-04-02 14:22:24,615 - INFO - Train Epoch: 7 [0/139 (0%)] Loss: 2.5360
125
+ 2025-04-02 14:22:32,474 - INFO - Train Epoch: 7 [10/139 (7%)] Loss: 2.5638
126
+ 2025-04-02 14:22:40,356 - INFO - Train Epoch: 7 [20/139 (14%)] Loss: 2.5746
127
+ 2025-04-02 14:22:48,327 - INFO - Train Epoch: 7 [30/139 (22%)] Loss: 2.6330
128
+ 2025-04-02 14:22:56,226 - INFO - Train Epoch: 7 [40/139 (29%)] Loss: 2.6069
129
+ 2025-04-02 14:23:04,179 - INFO - Train Epoch: 7 [50/139 (36%)] Loss: 2.5945
130
+ 2025-04-02 14:23:12,169 - INFO - Train Epoch: 7 [60/139 (43%)] Loss: 2.5539
131
+ 2025-04-02 14:23:20,235 - INFO - Train Epoch: 7 [70/139 (50%)] Loss: 2.5554
132
+ 2025-04-02 14:23:28,198 - INFO - Train Epoch: 7 [80/139 (58%)] Loss: 2.4784
133
+ 2025-04-02 14:23:36,143 - INFO - Train Epoch: 7 [90/139 (65%)] Loss: 2.4287
134
+ 2025-04-02 14:23:44,058 - INFO - Train Epoch: 7 [100/139 (72%)] Loss: 2.6228
135
+ 2025-04-02 14:23:52,034 - INFO - Train Epoch: 7 [110/139 (79%)] Loss: 2.4963
136
+ 2025-04-02 14:23:59,989 - INFO - Train Epoch: 7 [120/139 (86%)] Loss: 2.5964
137
+ 2025-04-02 14:24:07,921 - INFO - Train Epoch: 7 [130/139 (94%)] Loss: 2.6629
138
+ 2025-04-02 14:24:13,768 - INFO - Epoch 7 - Train Loss: 2.5792
139
+ 2025-04-02 14:24:39,020 - INFO - Epoch 7 - Validation Accuracy: 29.49%
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+ 2025-04-02 14:24:39,022 - INFO - New best validation accuracy: 29.49%
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+ 2025-04-02 14:24:40,351 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-02 14:24:41,715 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:24:41,716 - INFO - Epoch 8/20
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+ 2025-04-02 14:24:43,661 - INFO - Train Epoch: 8 [0/139 (0%)] Loss: 2.6087
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+ 2025-04-02 14:24:51,652 - INFO - Train Epoch: 8 [10/139 (7%)] Loss: 2.5279
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+ 2025-04-02 14:24:59,544 - INFO - Train Epoch: 8 [20/139 (14%)] Loss: 2.5207
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+ 2025-04-02 14:25:07,593 - INFO - Train Epoch: 8 [30/139 (22%)] Loss: 2.5255
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+ 2025-04-02 14:25:15,378 - INFO - Train Epoch: 8 [40/139 (29%)] Loss: 2.5060
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+ 2025-04-02 14:25:23,287 - INFO - Train Epoch: 8 [50/139 (36%)] Loss: 2.4811
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+ 2025-04-02 14:25:31,151 - INFO - Train Epoch: 8 [60/139 (43%)] Loss: 2.5239
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+ 2025-04-02 14:25:38,985 - INFO - Train Epoch: 8 [70/139 (50%)] Loss: 2.4759
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+ 2025-04-02 14:25:46,802 - INFO - Train Epoch: 8 [80/139 (58%)] Loss: 2.6370
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+ 2025-04-02 14:25:54,785 - INFO - Train Epoch: 8 [90/139 (65%)] Loss: 2.5335
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+ 2025-04-02 14:26:02,624 - INFO - Train Epoch: 8 [100/139 (72%)] Loss: 2.4911
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+ 2025-04-02 14:26:10,570 - INFO - Train Epoch: 8 [110/139 (79%)] Loss: 2.5112
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+ 2025-04-02 14:26:18,575 - INFO - Train Epoch: 8 [120/139 (86%)] Loss: 2.4290
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+ 2025-04-02 14:26:26,461 - INFO - Train Epoch: 8 [130/139 (94%)] Loss: 2.5473
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+ 2025-04-02 14:26:32,242 - INFO - Epoch 8 - Train Loss: 2.5068
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+ 2025-04-02 14:26:57,435 - INFO - Epoch 8 - Validation Accuracy: 31.17%
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+ 2025-04-02 14:26:57,436 - INFO - New best validation accuracy: 31.17%
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+ 2025-04-02 14:26:58,947 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-02 14:27:00,206 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:27:00,207 - INFO - Epoch 9/20
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+ 2025-04-02 14:27:02,778 - INFO - Train Epoch: 9 [0/139 (0%)] Loss: 2.5377
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+ 2025-04-02 14:27:10,569 - INFO - Train Epoch: 9 [10/139 (7%)] Loss: 2.4340
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+ 2025-04-02 14:27:18,373 - INFO - Train Epoch: 9 [20/139 (14%)] Loss: 2.4468
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+ 2025-04-02 14:27:26,329 - INFO - Train Epoch: 9 [30/139 (22%)] Loss: 2.4994
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+ 2025-04-02 14:27:34,247 - INFO - Train Epoch: 9 [40/139 (29%)] Loss: 2.2930
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+ 2025-04-02 14:27:42,104 - INFO - Train Epoch: 9 [50/139 (36%)] Loss: 2.4001
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+ 2025-04-02 14:27:49,982 - INFO - Train Epoch: 9 [60/139 (43%)] Loss: 2.4157
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+ 2025-04-02 14:27:57,895 - INFO - Train Epoch: 9 [70/139 (50%)] Loss: 2.4382
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+ 2025-04-02 14:28:05,796 - INFO - Train Epoch: 9 [80/139 (58%)] Loss: 2.5183
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+ 2025-04-02 14:28:13,747 - INFO - Train Epoch: 9 [90/139 (65%)] Loss: 2.2623
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+ 2025-04-02 14:28:21,760 - INFO - Train Epoch: 9 [100/139 (72%)] Loss: 2.3935
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+ 2025-04-02 14:28:29,686 - INFO - Train Epoch: 9 [110/139 (79%)] Loss: 2.3338
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+ 2025-04-02 14:28:37,765 - INFO - Train Epoch: 9 [120/139 (86%)] Loss: 2.4298
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+ 2025-04-02 14:28:45,919 - INFO - Train Epoch: 9 [130/139 (94%)] Loss: 2.5325
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+ 2025-04-02 14:28:51,887 - INFO - Epoch 9 - Train Loss: 2.4299
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+ 2025-04-02 14:29:17,795 - INFO - Epoch 9 - Validation Accuracy: 34.27%
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+ 2025-04-02 14:29:17,795 - INFO - New best validation accuracy: 34.27%
181
+ 2025-04-02 14:29:19,677 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-02 14:29:21,016 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:29:21,017 - INFO - Epoch 10/20
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+ 2025-04-02 14:29:23,062 - INFO - Train Epoch: 10 [0/139 (0%)] Loss: 2.3259
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+ 2025-04-02 14:29:30,893 - INFO - Train Epoch: 10 [10/139 (7%)] Loss: 2.3142
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+ 2025-04-02 14:29:38,760 - INFO - Train Epoch: 10 [20/139 (14%)] Loss: 2.4074
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+ 2025-04-02 14:29:46,599 - INFO - Train Epoch: 10 [30/139 (22%)] Loss: 2.3068
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+ 2025-04-02 14:29:54,705 - INFO - Train Epoch: 10 [40/139 (29%)] Loss: 2.4340
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+ 2025-04-02 14:30:02,641 - INFO - Train Epoch: 10 [50/139 (36%)] Loss: 2.3323
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+ 2025-04-02 14:30:10,596 - INFO - Train Epoch: 10 [60/139 (43%)] Loss: 2.4857
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+ 2025-04-02 14:30:18,597 - INFO - Train Epoch: 10 [70/139 (50%)] Loss: 2.3940
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+ 2025-04-02 14:30:26,574 - INFO - Train Epoch: 10 [80/139 (58%)] Loss: 2.3198
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+ 2025-04-02 14:30:34,548 - INFO - Train Epoch: 10 [90/139 (65%)] Loss: 2.2988
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+ 2025-04-02 14:30:42,457 - INFO - Train Epoch: 10 [100/139 (72%)] Loss: 2.3701
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+ 2025-04-02 14:30:50,343 - INFO - Train Epoch: 10 [110/139 (79%)] Loss: 2.4003
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+ 2025-04-02 14:30:58,453 - INFO - Train Epoch: 10 [120/139 (86%)] Loss: 2.2462
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+ 2025-04-02 14:31:06,332 - INFO - Train Epoch: 10 [130/139 (94%)] Loss: 2.3415
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+ 2025-04-02 14:31:12,202 - INFO - Epoch 10 - Train Loss: 2.3389
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+ 2025-04-02 14:31:38,925 - INFO - Epoch 10 - Validation Accuracy: 33.31%
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+ 2025-04-02 14:31:40,435 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:31:40,436 - INFO - Epoch 11/20
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+ 2025-04-02 14:31:42,480 - INFO - Train Epoch: 11 [0/139 (0%)] Loss: 2.2654
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+ 2025-04-02 14:31:50,346 - INFO - Train Epoch: 11 [10/139 (7%)] Loss: 2.2970
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+ 2025-04-02 14:31:58,473 - INFO - Train Epoch: 11 [20/139 (14%)] Loss: 2.3045
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+ 2025-04-02 14:32:06,487 - INFO - Train Epoch: 11 [30/139 (22%)] Loss: 2.3527
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+ 2025-04-02 14:32:14,756 - INFO - Train Epoch: 11 [40/139 (29%)] Loss: 2.2589
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+ 2025-04-02 14:32:22,860 - INFO - Train Epoch: 11 [50/139 (36%)] Loss: 2.2087
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+ 2025-04-02 14:32:31,009 - INFO - Train Epoch: 11 [60/139 (43%)] Loss: 2.3182
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+ 2025-04-02 14:32:39,060 - INFO - Train Epoch: 11 [70/139 (50%)] Loss: 2.1821
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+ 2025-04-02 14:32:47,174 - INFO - Train Epoch: 11 [80/139 (58%)] Loss: 2.2515
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+ 2025-04-02 14:32:55,311 - INFO - Train Epoch: 11 [90/139 (65%)] Loss: 2.2542
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+ 2025-04-02 14:33:03,449 - INFO - Train Epoch: 11 [100/139 (72%)] Loss: 2.1972
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+ 2025-04-02 14:33:11,523 - INFO - Train Epoch: 11 [110/139 (79%)] Loss: 2.2127
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+ 2025-04-02 14:33:19,549 - INFO - Train Epoch: 11 [120/139 (86%)] Loss: 2.2104
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+ 2025-04-02 14:33:27,721 - INFO - Train Epoch: 11 [130/139 (94%)] Loss: 2.2698
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+ 2025-04-02 14:33:33,917 - INFO - Epoch 11 - Train Loss: 2.2597
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+ 2025-04-02 14:34:00,394 - INFO - Epoch 11 - Validation Accuracy: 37.89%
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+ 2025-04-02 14:34:00,394 - INFO - New best validation accuracy: 37.89%
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+ 2025-04-02 14:34:02,390 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-02 14:34:03,646 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:34:03,646 - INFO - Epoch 12/20
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+ 2025-04-02 14:34:06,077 - INFO - Train Epoch: 12 [0/139 (0%)] Loss: 2.1203
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+ 2025-04-02 14:34:14,016 - INFO - Train Epoch: 12 [10/139 (7%)] Loss: 2.2320
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+ 2025-04-02 14:34:22,078 - INFO - Train Epoch: 12 [20/139 (14%)] Loss: 2.2467
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+ 2025-04-02 14:34:30,304 - INFO - Train Epoch: 12 [30/139 (22%)] Loss: 2.1849
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+ 2025-04-02 14:34:38,305 - INFO - Train Epoch: 12 [40/139 (29%)] Loss: 2.2505
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+ 2025-04-02 14:34:46,333 - INFO - Train Epoch: 12 [50/139 (36%)] Loss: 2.2347
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+ 2025-04-02 14:34:54,549 - INFO - Train Epoch: 12 [60/139 (43%)] Loss: 2.1159
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+ 2025-04-02 14:35:02,640 - INFO - Train Epoch: 12 [70/139 (50%)] Loss: 2.2158
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+ 2025-04-02 14:35:10,613 - INFO - Train Epoch: 12 [80/139 (58%)] Loss: 2.2528
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+ 2025-04-02 14:35:18,586 - INFO - Train Epoch: 12 [90/139 (65%)] Loss: 2.2577
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+ 2025-04-02 14:35:26,673 - INFO - Train Epoch: 12 [100/139 (72%)] Loss: 2.2492
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+ 2025-04-02 14:35:34,945 - INFO - Train Epoch: 12 [110/139 (79%)] Loss: 2.1906
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+ 2025-04-02 14:35:43,052 - INFO - Train Epoch: 12 [120/139 (86%)] Loss: 2.1317
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+ 2025-04-02 14:35:51,028 - INFO - Train Epoch: 12 [130/139 (94%)] Loss: 2.2154
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+ 2025-04-02 14:35:56,978 - INFO - Epoch 12 - Train Loss: 2.2076
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+ 2025-04-02 14:36:23,137 - INFO - Epoch 12 - Validation Accuracy: 37.75%
238
+ 2025-04-02 14:36:24,306 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:36:24,306 - INFO - Epoch 13/20
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+ 2025-04-02 14:36:26,469 - INFO - Train Epoch: 13 [0/139 (0%)] Loss: 2.2942
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+ 2025-04-02 14:36:34,691 - INFO - Train Epoch: 13 [10/139 (7%)] Loss: 2.1990
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+ 2025-04-02 14:36:43,071 - INFO - Train Epoch: 13 [20/139 (14%)] Loss: 2.2838
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+ 2025-04-02 14:36:51,320 - INFO - Train Epoch: 13 [30/139 (22%)] Loss: 2.1040
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+ 2025-04-02 14:36:59,453 - INFO - Train Epoch: 13 [40/139 (29%)] Loss: 2.1719
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+ 2025-04-02 14:37:07,562 - INFO - Train Epoch: 13 [50/139 (36%)] Loss: 2.3142
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+ 2025-04-02 14:37:15,538 - INFO - Train Epoch: 13 [60/139 (43%)] Loss: 2.1971
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+ 2025-04-02 14:37:23,899 - INFO - Train Epoch: 13 [70/139 (50%)] Loss: 2.1866
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+ 2025-04-02 14:37:31,914 - INFO - Train Epoch: 13 [80/139 (58%)] Loss: 2.0487
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+ 2025-04-02 14:37:40,043 - INFO - Train Epoch: 13 [90/139 (65%)] Loss: 2.1672
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+ 2025-04-02 14:37:48,191 - INFO - Train Epoch: 13 [100/139 (72%)] Loss: 2.2421
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+ 2025-04-02 14:37:56,331 - INFO - Train Epoch: 13 [110/139 (79%)] Loss: 2.2110
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+ 2025-04-02 14:38:04,329 - INFO - Train Epoch: 13 [120/139 (86%)] Loss: 2.1149
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+ 2025-04-02 14:38:12,589 - INFO - Train Epoch: 13 [130/139 (94%)] Loss: 2.2597
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+ 2025-04-02 14:38:18,562 - INFO - Epoch 13 - Train Loss: 2.1678
255
+ 2025-04-02 14:38:43,817 - INFO - Epoch 13 - Validation Accuracy: 38.39%
256
+ 2025-04-02 14:38:43,817 - INFO - New best validation accuracy: 38.39%
257
+ 2025-04-02 14:38:45,966 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
258
+ 2025-04-02 14:38:47,453 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-02 14:38:47,453 - INFO - Epoch 14/20
260
+ 2025-04-02 14:38:49,536 - INFO - Train Epoch: 14 [0/139 (0%)] Loss: 2.2585
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+ 2025-04-02 14:38:57,789 - INFO - Train Epoch: 14 [10/139 (7%)] Loss: 2.1400
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+ 2025-04-02 14:39:05,863 - INFO - Train Epoch: 14 [20/139 (14%)] Loss: 2.1115
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+ 2025-04-02 14:39:14,132 - INFO - Train Epoch: 14 [30/139 (22%)] Loss: 2.2339
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+ 2025-04-02 14:39:22,256 - INFO - Train Epoch: 14 [40/139 (29%)] Loss: 2.1576
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+ 2025-04-02 14:39:30,343 - INFO - Train Epoch: 14 [50/139 (36%)] Loss: 2.0880
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+ 2025-04-02 14:39:38,508 - INFO - Train Epoch: 14 [60/139 (43%)] Loss: 2.2093
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+ 2025-04-02 14:39:46,402 - INFO - Train Epoch: 14 [70/139 (50%)] Loss: 2.1430
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+ 2025-04-02 14:39:54,332 - INFO - Train Epoch: 14 [80/139 (58%)] Loss: 2.2418
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+ 2025-04-02 14:40:02,282 - INFO - Train Epoch: 14 [90/139 (65%)] Loss: 2.1249
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+ 2025-04-02 14:40:10,287 - INFO - Train Epoch: 14 [100/139 (72%)] Loss: 2.1342
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+ 2025-04-02 14:40:18,378 - INFO - Train Epoch: 14 [110/139 (79%)] Loss: 2.1652
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+ 2025-04-02 14:40:26,443 - INFO - Train Epoch: 14 [120/139 (86%)] Loss: 2.1205
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+ 2025-04-02 14:40:34,484 - INFO - Train Epoch: 14 [130/139 (94%)] Loss: 2.0860
274
+ 2025-04-02 14:40:40,468 - INFO - Epoch 14 - Train Loss: 2.1357
275
+ 2025-04-02 14:41:05,763 - INFO - Epoch 14 - Validation Accuracy: 39.94%
276
+ 2025-04-02 14:41:05,764 - INFO - New best validation accuracy: 39.94%
277
+ 2025-04-02 14:41:07,876 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
278
+ 2025-04-02 14:41:09,234 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
279
+ 2025-04-02 14:41:09,234 - INFO - Epoch 15/20
280
+ 2025-04-02 14:41:11,265 - INFO - Train Epoch: 15 [0/139 (0%)] Loss: 2.0835
281
+ 2025-04-02 14:41:19,350 - INFO - Train Epoch: 15 [10/139 (7%)] Loss: 2.2302
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+ 2025-04-02 14:41:27,253 - INFO - Train Epoch: 15 [20/139 (14%)] Loss: 2.2401
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+ 2025-04-02 14:41:35,141 - INFO - Train Epoch: 15 [30/139 (22%)] Loss: 2.0798
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+ 2025-04-02 14:41:42,923 - INFO - Train Epoch: 15 [40/139 (29%)] Loss: 2.0921
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+ 2025-04-02 14:41:50,709 - INFO - Train Epoch: 15 [50/139 (36%)] Loss: 2.1230
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+ 2025-04-02 14:41:58,706 - INFO - Train Epoch: 15 [60/139 (43%)] Loss: 2.0546
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+ 2025-04-02 14:42:06,484 - INFO - Train Epoch: 15 [70/139 (50%)] Loss: 2.1091
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+ 2025-04-02 14:42:14,408 - INFO - Train Epoch: 15 [80/139 (58%)] Loss: 2.2119
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+ 2025-04-02 14:42:22,220 - INFO - Train Epoch: 15 [90/139 (65%)] Loss: 2.0504
290
+ 2025-04-02 14:42:30,033 - INFO - Train Epoch: 15 [100/139 (72%)] Loss: 2.0775
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+ 2025-04-02 14:42:38,051 - INFO - Train Epoch: 15 [110/139 (79%)] Loss: 2.0327
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+ 2025-04-02 14:42:46,050 - INFO - Train Epoch: 15 [120/139 (86%)] Loss: 2.0069
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+ 2025-04-02 14:42:53,909 - INFO - Train Epoch: 15 [130/139 (94%)] Loss: 2.0370
294
+ 2025-04-02 14:43:00,090 - INFO - Epoch 15 - Train Loss: 2.1101
295
+ 2025-04-02 14:43:23,852 - INFO - Epoch 15 - Validation Accuracy: 41.25%
296
+ 2025-04-02 14:43:23,852 - INFO - New best validation accuracy: 41.25%
297
+ 2025-04-02 14:43:25,786 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
298
+ 2025-04-02 14:43:27,464 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
299
+ 2025-04-02 14:43:27,464 - INFO - Epoch 16/20
300
+ 2025-04-02 14:43:29,743 - INFO - Train Epoch: 16 [0/139 (0%)] Loss: 2.1173
301
+ 2025-04-02 14:43:37,666 - INFO - Train Epoch: 16 [10/139 (7%)] Loss: 2.1729
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+ 2025-04-02 14:43:45,379 - INFO - Train Epoch: 16 [20/139 (14%)] Loss: 2.0460
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+ 2025-04-02 14:43:53,311 - INFO - Train Epoch: 16 [30/139 (22%)] Loss: 2.0976
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+ 2025-04-02 14:44:01,168 - INFO - Train Epoch: 16 [40/139 (29%)] Loss: 2.0093
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+ 2025-04-02 14:44:08,955 - INFO - Train Epoch: 16 [50/139 (36%)] Loss: 2.0967
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+ 2025-04-02 14:44:16,679 - INFO - Train Epoch: 16 [60/139 (43%)] Loss: 2.0645
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+ 2025-04-02 14:44:24,414 - INFO - Train Epoch: 16 [70/139 (50%)] Loss: 2.0432
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+ 2025-04-02 14:44:32,331 - INFO - Train Epoch: 16 [80/139 (58%)] Loss: 2.0796
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+ 2025-04-02 14:44:40,142 - INFO - Train Epoch: 16 [90/139 (65%)] Loss: 2.0971
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+ 2025-04-02 14:44:48,187 - INFO - Train Epoch: 16 [100/139 (72%)] Loss: 2.0825
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+ 2025-04-02 14:44:56,054 - INFO - Train Epoch: 16 [110/139 (79%)] Loss: 2.1678
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+ 2025-04-02 14:45:03,938 - INFO - Train Epoch: 16 [120/139 (86%)] Loss: 2.0806
313
+ 2025-04-02 14:45:11,718 - INFO - Train Epoch: 16 [130/139 (94%)] Loss: 2.0961
314
+ 2025-04-02 14:45:17,428 - INFO - Epoch 16 - Train Loss: 2.0878
315
+ 2025-04-02 14:45:41,506 - INFO - Epoch 16 - Validation Accuracy: 41.72%
316
+ 2025-04-02 14:45:41,506 - INFO - New best validation accuracy: 41.72%
317
+ 2025-04-02 14:45:43,591 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
318
+ 2025-04-02 14:45:44,981 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
319
+ 2025-04-02 14:45:44,981 - INFO - Epoch 17/20
320
+ 2025-04-02 14:45:47,395 - INFO - Train Epoch: 17 [0/139 (0%)] Loss: 1.9595
321
+ 2025-04-02 14:45:55,237 - INFO - Train Epoch: 17 [10/139 (7%)] Loss: 2.1881
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+ 2025-04-02 14:46:03,042 - INFO - Train Epoch: 17 [20/139 (14%)] Loss: 1.9786
323
+ 2025-04-02 14:46:10,781 - INFO - Train Epoch: 17 [30/139 (22%)] Loss: 2.1170
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+ 2025-04-02 14:46:18,600 - INFO - Train Epoch: 17 [40/139 (29%)] Loss: 2.0735
325
+ 2025-04-02 14:46:26,492 - INFO - Train Epoch: 17 [50/139 (36%)] Loss: 2.0401
326
+ 2025-04-02 14:46:34,319 - INFO - Train Epoch: 17 [60/139 (43%)] Loss: 2.1358
327
+ 2025-04-02 14:46:42,160 - INFO - Train Epoch: 17 [70/139 (50%)] Loss: 1.9806
328
+ 2025-04-02 14:46:49,965 - INFO - Train Epoch: 17 [80/139 (58%)] Loss: 2.0478
329
+ 2025-04-02 14:46:57,764 - INFO - Train Epoch: 17 [90/139 (65%)] Loss: 2.0375
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+ 2025-04-02 14:47:05,639 - INFO - Train Epoch: 17 [100/139 (72%)] Loss: 1.9852
331
+ 2025-04-02 14:47:13,489 - INFO - Train Epoch: 17 [110/139 (79%)] Loss: 2.0780
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+ 2025-04-02 14:47:21,270 - INFO - Train Epoch: 17 [120/139 (86%)] Loss: 2.0180
333
+ 2025-04-02 14:47:29,100 - INFO - Train Epoch: 17 [130/139 (94%)] Loss: 2.0758
334
+ 2025-04-02 14:47:34,823 - INFO - Epoch 17 - Train Loss: 2.0673
335
+ 2025-04-02 14:47:58,370 - INFO - Epoch 17 - Validation Accuracy: 41.19%
336
+ 2025-04-02 14:47:59,761 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
337
+ 2025-04-02 14:47:59,761 - INFO - Epoch 18/20
338
+ 2025-04-02 14:48:01,814 - INFO - Train Epoch: 18 [0/139 (0%)] Loss: 2.1478
339
+ 2025-04-02 14:48:09,665 - INFO - Train Epoch: 18 [10/139 (7%)] Loss: 2.0207
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+ 2025-04-02 14:48:17,355 - INFO - Train Epoch: 18 [20/139 (14%)] Loss: 2.0788
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+ 2025-04-02 14:48:25,267 - INFO - Train Epoch: 18 [30/139 (22%)] Loss: 2.1084
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+ 2025-04-02 14:48:33,026 - INFO - Train Epoch: 18 [40/139 (29%)] Loss: 1.9776
343
+ 2025-04-02 14:48:40,985 - INFO - Train Epoch: 18 [50/139 (36%)] Loss: 2.0759
344
+ 2025-04-02 14:48:48,809 - INFO - Train Epoch: 18 [60/139 (43%)] Loss: 1.9244
345
+ 2025-04-02 14:48:56,743 - INFO - Train Epoch: 18 [70/139 (50%)] Loss: 2.1015
346
+ 2025-04-02 14:49:04,737 - INFO - Train Epoch: 18 [80/139 (58%)] Loss: 1.9120
347
+ 2025-04-02 14:49:12,708 - INFO - Train Epoch: 18 [90/139 (65%)] Loss: 1.9957
348
+ 2025-04-02 14:49:20,710 - INFO - Train Epoch: 18 [100/139 (72%)] Loss: 2.0247
349
+ 2025-04-02 14:49:28,589 - INFO - Train Epoch: 18 [110/139 (79%)] Loss: 1.9843
350
+ 2025-04-02 14:49:36,448 - INFO - Train Epoch: 18 [120/139 (86%)] Loss: 2.0736
351
+ 2025-04-02 14:49:44,284 - INFO - Train Epoch: 18 [130/139 (94%)] Loss: 1.9569
352
+ 2025-04-02 14:49:50,135 - INFO - Epoch 18 - Train Loss: 2.0515
353
+ 2025-04-02 14:50:14,383 - INFO - Epoch 18 - Validation Accuracy: 41.14%
354
+ 2025-04-02 14:50:15,836 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
355
+ 2025-04-02 14:50:15,836 - INFO - Epoch 19/20
356
+ 2025-04-02 14:50:17,955 - INFO - Train Epoch: 19 [0/139 (0%)] Loss: 2.1049
357
+ 2025-04-02 14:50:25,843 - INFO - Train Epoch: 19 [10/139 (7%)] Loss: 2.0481
358
+ 2025-04-02 14:50:33,875 - INFO - Train Epoch: 19 [20/139 (14%)] Loss: 2.0734
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+ 2025-04-02 14:50:41,938 - INFO - Train Epoch: 19 [30/139 (22%)] Loss: 2.0229
360
+ 2025-04-02 14:50:49,936 - INFO - Train Epoch: 19 [40/139 (29%)] Loss: 2.0199
361
+ 2025-04-02 14:50:57,905 - INFO - Train Epoch: 19 [50/139 (36%)] Loss: 2.0320
362
+ 2025-04-02 14:51:05,830 - INFO - Train Epoch: 19 [60/139 (43%)] Loss: 2.0463
363
+ 2025-04-02 14:51:13,752 - INFO - Train Epoch: 19 [70/139 (50%)] Loss: 2.1244
364
+ 2025-04-02 14:51:21,593 - INFO - Train Epoch: 19 [80/139 (58%)] Loss: 2.0427
365
+ 2025-04-02 14:51:29,646 - INFO - Train Epoch: 19 [90/139 (65%)] Loss: 2.0550
366
+ 2025-04-02 14:51:37,536 - INFO - Train Epoch: 19 [100/139 (72%)] Loss: 2.0762
367
+ 2025-04-02 14:51:45,373 - INFO - Train Epoch: 19 [110/139 (79%)] Loss: 2.0074
368
+ 2025-04-02 14:51:53,254 - INFO - Train Epoch: 19 [120/139 (86%)] Loss: 1.9832
369
+ 2025-04-02 14:52:01,088 - INFO - Train Epoch: 19 [130/139 (94%)] Loss: 2.0020
370
+ 2025-04-02 14:52:06,872 - INFO - Epoch 19 - Train Loss: 2.0318
371
+ 2025-04-02 14:52:31,135 - INFO - Epoch 19 - Validation Accuracy: 41.17%
372
+ 2025-04-02 14:52:32,456 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
373
+ 2025-04-02 14:52:32,456 - INFO - Epoch 20/20
374
+ 2025-04-02 14:52:34,827 - INFO - Train Epoch: 20 [0/139 (0%)] Loss: 1.9814
375
+ 2025-04-02 14:52:42,642 - INFO - Train Epoch: 20 [10/139 (7%)] Loss: 1.9657
376
+ 2025-04-02 14:52:50,641 - INFO - Train Epoch: 20 [20/139 (14%)] Loss: 2.1401
377
+ 2025-04-02 14:52:58,656 - INFO - Train Epoch: 20 [30/139 (22%)] Loss: 2.0245
378
+ 2025-04-02 14:53:06,422 - INFO - Train Epoch: 20 [40/139 (29%)] Loss: 1.9865
379
+ 2025-04-02 14:53:14,289 - INFO - Train Epoch: 20 [50/139 (36%)] Loss: 2.0353
380
+ 2025-04-02 14:53:22,056 - INFO - Train Epoch: 20 [60/139 (43%)] Loss: 2.0007
381
+ 2025-04-02 14:53:29,894 - INFO - Train Epoch: 20 [70/139 (50%)] Loss: 2.1748
382
+ 2025-04-02 14:53:37,970 - INFO - Train Epoch: 20 [80/139 (58%)] Loss: 1.9197
383
+ 2025-04-02 14:53:45,883 - INFO - Train Epoch: 20 [90/139 (65%)] Loss: 1.9123
384
+ 2025-04-02 14:53:54,097 - INFO - Train Epoch: 20 [100/139 (72%)] Loss: 2.0517
385
+ 2025-04-02 14:54:02,098 - INFO - Train Epoch: 20 [110/139 (79%)] Loss: 2.0897
386
+ 2025-04-02 14:54:09,889 - INFO - Train Epoch: 20 [120/139 (86%)] Loss: 2.0286
387
+ 2025-04-02 14:54:17,770 - INFO - Train Epoch: 20 [130/139 (94%)] Loss: 2.0354
388
+ 2025-04-02 14:54:23,545 - INFO - Epoch 20 - Train Loss: 2.0172
389
+ 2025-04-02 14:54:47,509 - INFO - Epoch 20 - Validation Accuracy: 44.87%
390
+ 2025-04-02 14:54:47,509 - INFO - New best validation accuracy: 44.87%
391
+ 2025-04-02 14:54:49,401 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/best_model
392
+ 2025-04-02 14:54:50,889 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
393
+ 2025-04-02 14:55:14,711 - INFO - Final validation accuracy: 44.87%
394
+ 2025-04-02 14:55:14,712 - INFO - Best validation accuracy: 44.87%
395
+ 2025-04-02 14:55:16,095 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_100_percent/checkpoints/checkpoint_last
396
+ 2025-04-02 14:55:16,096 - INFO - Training completed
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1
+ 2025-04-03 00:56:25,948 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=0.1, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='resnet', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=6), gpu=6, seed=42, num_workers=4, output_dir='./outputs/exp2/resnet/fewshot_10_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
2
+ 2025-04-03 00:56:25,948 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
3
+ 2025-04-03 00:56:56,699 - INFO - Initializing RobustMedClip model
4
+ 2025-04-03 00:57:05,774 - INFO - Starting training
5
+ 2025-04-03 00:57:05,774 - INFO - Epoch 1/20
6
+ 2025-04-03 00:57:07,750 - INFO - Train Epoch: 1 [0/14 (0%)] Loss: 4.4771
7
+ 2025-04-03 00:57:15,350 - INFO - Train Epoch: 1 [10/14 (71%)] Loss: 4.3791
8
+ 2025-04-03 00:57:17,604 - INFO - Epoch 1 - Train Loss: 4.4437
9
+ 2025-04-03 00:57:40,645 - INFO - Epoch 1 - Validation Accuracy: 3.66%
10
+ 2025-04-03 00:57:40,646 - INFO - New best validation accuracy: 3.66%
11
+ 2025-04-03 00:57:41,791 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
12
+ 2025-04-03 00:57:42,930 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
13
+ 2025-04-03 00:57:42,930 - INFO - Epoch 2/20
14
+ 2025-04-03 00:57:44,577 - INFO - Train Epoch: 2 [0/14 (0%)] Loss: 4.3013
15
+ 2025-04-03 00:57:52,221 - INFO - Train Epoch: 2 [10/14 (71%)] Loss: 4.2776
16
+ 2025-04-03 00:57:54,476 - INFO - Epoch 2 - Train Loss: 4.3320
17
+ 2025-04-03 00:58:17,398 - INFO - Epoch 2 - Validation Accuracy: 5.49%
18
+ 2025-04-03 00:58:17,398 - INFO - New best validation accuracy: 5.49%
19
+ 2025-04-03 00:58:18,781 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
20
+ 2025-04-03 00:58:20,008 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
21
+ 2025-04-03 00:58:20,008 - INFO - Epoch 3/20
22
+ 2025-04-03 00:58:21,721 - INFO - Train Epoch: 3 [0/14 (0%)] Loss: 4.2289
23
+ 2025-04-03 00:58:29,367 - INFO - Train Epoch: 3 [10/14 (71%)] Loss: 4.0648
24
+ 2025-04-03 00:58:31,584 - INFO - Epoch 3 - Train Loss: 4.1831
25
+ 2025-04-03 00:58:54,591 - INFO - Epoch 3 - Validation Accuracy: 5.31%
26
+ 2025-04-03 00:58:55,945 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
27
+ 2025-04-03 00:58:55,945 - INFO - Epoch 4/20
28
+ 2025-04-03 00:58:57,652 - INFO - Train Epoch: 4 [0/14 (0%)] Loss: 4.1556
29
+ 2025-04-03 00:59:05,399 - INFO - Train Epoch: 4 [10/14 (71%)] Loss: 3.9845
30
+ 2025-04-03 00:59:07,649 - INFO - Epoch 4 - Train Loss: 4.0466
31
+ 2025-04-03 00:59:30,511 - INFO - Epoch 4 - Validation Accuracy: 5.53%
32
+ 2025-04-03 00:59:30,511 - INFO - New best validation accuracy: 5.53%
33
+ 2025-04-03 00:59:31,814 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
34
+ 2025-04-03 00:59:33,102 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
35
+ 2025-04-03 00:59:33,102 - INFO - Epoch 5/20
36
+ 2025-04-03 00:59:34,865 - INFO - Train Epoch: 5 [0/14 (0%)] Loss: 4.0378
37
+ 2025-04-03 00:59:42,507 - INFO - Train Epoch: 5 [10/14 (71%)] Loss: 3.9366
38
+ 2025-04-03 00:59:44,740 - INFO - Epoch 5 - Train Loss: 3.9703
39
+ 2025-04-03 01:00:07,722 - INFO - Epoch 5 - Validation Accuracy: 5.82%
40
+ 2025-04-03 01:00:07,722 - INFO - New best validation accuracy: 5.82%
41
+ 2025-04-03 01:00:09,008 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
42
+ 2025-04-03 01:00:10,232 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
43
+ 2025-04-03 01:00:10,233 - INFO - Epoch 6/20
44
+ 2025-04-03 01:00:11,946 - INFO - Train Epoch: 6 [0/14 (0%)] Loss: 4.0042
45
+ 2025-04-03 01:00:19,637 - INFO - Train Epoch: 6 [10/14 (71%)] Loss: 3.9399
46
+ 2025-04-03 01:00:21,887 - INFO - Epoch 6 - Train Loss: 3.9133
47
+ 2025-04-03 01:00:44,608 - INFO - Epoch 6 - Validation Accuracy: 6.36%
48
+ 2025-04-03 01:00:44,608 - INFO - New best validation accuracy: 6.36%
49
+ 2025-04-03 01:00:46,988 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
50
+ 2025-04-03 01:00:48,269 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
51
+ 2025-04-03 01:00:48,269 - INFO - Epoch 7/20
52
+ 2025-04-03 01:00:49,983 - INFO - Train Epoch: 7 [0/14 (0%)] Loss: 3.8369
53
+ 2025-04-03 01:00:57,643 - INFO - Train Epoch: 7 [10/14 (71%)] Loss: 3.8085
54
+ 2025-04-03 01:00:59,893 - INFO - Epoch 7 - Train Loss: 3.8794
55
+ 2025-04-03 01:01:22,923 - INFO - Epoch 7 - Validation Accuracy: 7.20%
56
+ 2025-04-03 01:01:22,923 - INFO - New best validation accuracy: 7.20%
57
+ 2025-04-03 01:01:24,243 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
58
+ 2025-04-03 01:01:25,488 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
59
+ 2025-04-03 01:01:25,488 - INFO - Epoch 8/20
60
+ 2025-04-03 01:01:27,178 - INFO - Train Epoch: 8 [0/14 (0%)] Loss: 3.9095
61
+ 2025-04-03 01:01:34,823 - INFO - Train Epoch: 8 [10/14 (71%)] Loss: 3.7926
62
+ 2025-04-03 01:01:37,074 - INFO - Epoch 8 - Train Loss: 3.8377
63
+ 2025-04-03 01:01:59,945 - INFO - Epoch 8 - Validation Accuracy: 8.14%
64
+ 2025-04-03 01:01:59,946 - INFO - New best validation accuracy: 8.14%
65
+ 2025-04-03 01:02:01,291 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
66
+ 2025-04-03 01:02:02,491 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
67
+ 2025-04-03 01:02:02,491 - INFO - Epoch 9/20
68
+ 2025-04-03 01:02:04,195 - INFO - Train Epoch: 9 [0/14 (0%)] Loss: 3.8417
69
+ 2025-04-03 01:02:11,849 - INFO - Train Epoch: 9 [10/14 (71%)] Loss: 3.8035
70
+ 2025-04-03 01:02:14,093 - INFO - Epoch 9 - Train Loss: 3.7930
71
+ 2025-04-03 01:02:37,081 - INFO - Epoch 9 - Validation Accuracy: 8.77%
72
+ 2025-04-03 01:02:37,081 - INFO - New best validation accuracy: 8.77%
73
+ 2025-04-03 01:02:38,423 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
74
+ 2025-04-03 01:02:39,651 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
75
+ 2025-04-03 01:02:39,651 - INFO - Epoch 10/20
76
+ 2025-04-03 01:02:41,334 - INFO - Train Epoch: 10 [0/14 (0%)] Loss: 3.7442
77
+ 2025-04-03 01:02:48,997 - INFO - Train Epoch: 10 [10/14 (71%)] Loss: 3.7480
78
+ 2025-04-03 01:02:51,257 - INFO - Epoch 10 - Train Loss: 3.7501
79
+ 2025-04-03 01:03:14,202 - INFO - Epoch 10 - Validation Accuracy: 9.10%
80
+ 2025-04-03 01:03:14,202 - INFO - New best validation accuracy: 9.10%
81
+ 2025-04-03 01:03:15,499 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
82
+ 2025-04-03 01:03:16,753 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
83
+ 2025-04-03 01:03:16,754 - INFO - Epoch 11/20
84
+ 2025-04-03 01:03:18,414 - INFO - Train Epoch: 11 [0/14 (0%)] Loss: 3.6758
85
+ 2025-04-03 01:03:26,095 - INFO - Train Epoch: 11 [10/14 (71%)] Loss: 3.6815
86
+ 2025-04-03 01:03:28,342 - INFO - Epoch 11 - Train Loss: 3.6950
87
+ 2025-04-03 01:03:51,270 - INFO - Epoch 11 - Validation Accuracy: 9.61%
88
+ 2025-04-03 01:03:51,271 - INFO - New best validation accuracy: 9.61%
89
+ 2025-04-03 01:03:52,684 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
90
+ 2025-04-03 01:03:54,068 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
91
+ 2025-04-03 01:03:54,068 - INFO - Epoch 12/20
92
+ 2025-04-03 01:03:55,858 - INFO - Train Epoch: 12 [0/14 (0%)] Loss: 3.6792
93
+ 2025-04-03 01:04:03,513 - INFO - Train Epoch: 12 [10/14 (71%)] Loss: 3.6223
94
+ 2025-04-03 01:04:05,755 - INFO - Epoch 12 - Train Loss: 3.6420
95
+ 2025-04-03 01:04:28,644 - INFO - Epoch 12 - Validation Accuracy: 10.36%
96
+ 2025-04-03 01:04:28,644 - INFO - New best validation accuracy: 10.36%
97
+ 2025-04-03 01:04:29,918 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
98
+ 2025-04-03 01:04:31,320 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
99
+ 2025-04-03 01:04:31,320 - INFO - Epoch 13/20
100
+ 2025-04-03 01:04:33,100 - INFO - Train Epoch: 13 [0/14 (0%)] Loss: 3.5838
101
+ 2025-04-03 01:04:40,776 - INFO - Train Epoch: 13 [10/14 (71%)] Loss: 3.5769
102
+ 2025-04-03 01:04:43,051 - INFO - Epoch 13 - Train Loss: 3.5987
103
+ 2025-04-03 01:05:06,073 - INFO - Epoch 13 - Validation Accuracy: 11.44%
104
+ 2025-04-03 01:05:06,073 - INFO - New best validation accuracy: 11.44%
105
+ 2025-04-03 01:05:07,399 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
106
+ 2025-04-03 01:05:08,651 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
107
+ 2025-04-03 01:05:08,651 - INFO - Epoch 14/20
108
+ 2025-04-03 01:05:10,351 - INFO - Train Epoch: 14 [0/14 (0%)] Loss: 3.5676
109
+ 2025-04-03 01:05:18,019 - INFO - Train Epoch: 14 [10/14 (71%)] Loss: 3.5488
110
+ 2025-04-03 01:05:20,296 - INFO - Epoch 14 - Train Loss: 3.5675
111
+ 2025-04-03 01:05:43,004 - INFO - Epoch 14 - Validation Accuracy: 12.14%
112
+ 2025-04-03 01:05:43,005 - INFO - New best validation accuracy: 12.14%
113
+ 2025-04-03 01:05:44,331 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
114
+ 2025-04-03 01:05:45,671 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
115
+ 2025-04-03 01:05:45,671 - INFO - Epoch 15/20
116
+ 2025-04-03 01:05:47,391 - INFO - Train Epoch: 15 [0/14 (0%)] Loss: 3.4755
117
+ 2025-04-03 01:05:55,066 - INFO - Train Epoch: 15 [10/14 (71%)] Loss: 3.5211
118
+ 2025-04-03 01:05:57,292 - INFO - Epoch 15 - Train Loss: 3.5283
119
+ 2025-04-03 01:06:20,264 - INFO - Epoch 15 - Validation Accuracy: 12.11%
120
+ 2025-04-03 01:06:21,468 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
121
+ 2025-04-03 01:06:21,468 - INFO - Epoch 16/20
122
+ 2025-04-03 01:06:23,214 - INFO - Train Epoch: 16 [0/14 (0%)] Loss: 3.4649
123
+ 2025-04-03 01:06:30,858 - INFO - Train Epoch: 16 [10/14 (71%)] Loss: 3.5502
124
+ 2025-04-03 01:06:33,121 - INFO - Epoch 16 - Train Loss: 3.4946
125
+ 2025-04-03 01:06:55,923 - INFO - Epoch 16 - Validation Accuracy: 12.42%
126
+ 2025-04-03 01:06:55,923 - INFO - New best validation accuracy: 12.42%
127
+ 2025-04-03 01:06:57,290 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
128
+ 2025-04-03 01:06:58,541 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
129
+ 2025-04-03 01:06:58,541 - INFO - Epoch 17/20
130
+ 2025-04-03 01:07:00,224 - INFO - Train Epoch: 17 [0/14 (0%)] Loss: 3.5104
131
+ 2025-04-03 01:07:07,901 - INFO - Train Epoch: 17 [10/14 (71%)] Loss: 3.4328
132
+ 2025-04-03 01:07:10,149 - INFO - Epoch 17 - Train Loss: 3.4582
133
+ 2025-04-03 01:07:33,260 - INFO - Epoch 17 - Validation Accuracy: 14.26%
134
+ 2025-04-03 01:07:33,260 - INFO - New best validation accuracy: 14.26%
135
+ 2025-04-03 01:07:34,601 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
136
+ 2025-04-03 01:07:35,815 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
137
+ 2025-04-03 01:07:35,815 - INFO - Epoch 18/20
138
+ 2025-04-03 01:07:37,491 - INFO - Train Epoch: 18 [0/14 (0%)] Loss: 3.6166
139
+ 2025-04-03 01:07:45,136 - INFO - Train Epoch: 18 [10/14 (71%)] Loss: 3.3389
140
+ 2025-04-03 01:07:47,386 - INFO - Epoch 18 - Train Loss: 3.4320
141
+ 2025-04-03 01:08:10,252 - INFO - Epoch 18 - Validation Accuracy: 14.88%
142
+ 2025-04-03 01:08:10,252 - INFO - New best validation accuracy: 14.88%
143
+ 2025-04-03 01:08:11,692 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
144
+ 2025-04-03 01:08:12,918 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
145
+ 2025-04-03 01:08:12,918 - INFO - Epoch 19/20
146
+ 2025-04-03 01:08:14,609 - INFO - Train Epoch: 19 [0/14 (0%)] Loss: 3.4113
147
+ 2025-04-03 01:08:22,254 - INFO - Train Epoch: 19 [10/14 (71%)] Loss: 3.3562
148
+ 2025-04-03 01:08:24,498 - INFO - Epoch 19 - Train Loss: 3.4072
149
+ 2025-04-03 01:08:47,486 - INFO - Epoch 19 - Validation Accuracy: 16.43%
150
+ 2025-04-03 01:08:47,486 - INFO - New best validation accuracy: 16.43%
151
+ 2025-04-03 01:08:48,848 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
152
+ 2025-04-03 01:08:50,063 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
153
+ 2025-04-03 01:08:50,063 - INFO - Epoch 20/20
154
+ 2025-04-03 01:08:51,749 - INFO - Train Epoch: 20 [0/14 (0%)] Loss: 3.2683
155
+ 2025-04-03 01:08:59,398 - INFO - Train Epoch: 20 [10/14 (71%)] Loss: 3.3672
156
+ 2025-04-03 01:09:01,635 - INFO - Epoch 20 - Train Loss: 3.3749
157
+ 2025-04-03 01:09:25,044 - INFO - Epoch 20 - Validation Accuracy: 18.96%
158
+ 2025-04-03 01:09:25,044 - INFO - New best validation accuracy: 18.96%
159
+ 2025-04-03 01:09:26,404 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/best_model
160
+ 2025-04-03 01:09:27,751 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
161
+ 2025-04-03 01:09:50,801 - INFO - Final validation accuracy: 18.96%
162
+ 2025-04-03 01:09:50,801 - INFO - Best validation accuracy: 18.96%
163
+ 2025-04-03 01:09:51,991 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_10_percent/checkpoints/checkpoint_last
164
+ 2025-04-03 01:09:51,991 - INFO - Training completed
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1
+ 2025-04-03 01:10:01,371 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=0.3, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='resnet', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=6), gpu=6, seed=42, num_workers=4, output_dir='./outputs/exp2/resnet/fewshot_30_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
2
+ 2025-04-03 01:10:01,371 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
3
+ 2025-04-03 01:10:32,457 - INFO - Initializing RobustMedClip model
4
+ 2025-04-03 01:10:38,945 - INFO - Starting training
5
+ 2025-04-03 01:10:38,946 - INFO - Epoch 1/20
6
+ 2025-04-03 01:10:40,862 - INFO - Train Epoch: 1 [0/42 (0%)] Loss: 4.5854
7
+ 2025-04-03 01:10:48,402 - INFO - Train Epoch: 1 [10/42 (24%)] Loss: 4.3735
8
+ 2025-04-03 01:10:55,932 - INFO - Train Epoch: 1 [20/42 (48%)] Loss: 4.2601
9
+ 2025-04-03 01:11:03,569 - INFO - Train Epoch: 1 [30/42 (71%)] Loss: 4.2088
10
+ 2025-04-03 01:11:11,182 - INFO - Train Epoch: 1 [40/42 (95%)] Loss: 4.2444
11
+ 2025-04-03 01:11:11,607 - INFO - Epoch 1 - Train Loss: 4.3176
12
+ 2025-04-03 01:11:34,203 - INFO - Epoch 1 - Validation Accuracy: 5.26%
13
+ 2025-04-03 01:11:34,204 - INFO - New best validation accuracy: 5.26%
14
+ 2025-04-03 01:11:35,414 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
15
+ 2025-04-03 01:11:36,540 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
16
+ 2025-04-03 01:11:36,540 - INFO - Epoch 2/20
17
+ 2025-04-03 01:11:38,120 - INFO - Train Epoch: 2 [0/42 (0%)] Loss: 4.1283
18
+ 2025-04-03 01:11:45,805 - INFO - Train Epoch: 2 [10/42 (24%)] Loss: 4.0279
19
+ 2025-04-03 01:11:53,469 - INFO - Train Epoch: 2 [20/42 (48%)] Loss: 3.9734
20
+ 2025-04-03 01:12:01,138 - INFO - Train Epoch: 2 [30/42 (71%)] Loss: 3.9627
21
+ 2025-04-03 01:12:08,811 - INFO - Train Epoch: 2 [40/42 (95%)] Loss: 3.8872
22
+ 2025-04-03 01:12:09,243 - INFO - Epoch 2 - Train Loss: 3.9887
23
+ 2025-04-03 01:12:32,576 - INFO - Epoch 2 - Validation Accuracy: 6.88%
24
+ 2025-04-03 01:12:32,576 - INFO - New best validation accuracy: 6.88%
25
+ 2025-04-03 01:12:33,898 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
26
+ 2025-04-03 01:12:35,133 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
27
+ 2025-04-03 01:12:35,133 - INFO - Epoch 3/20
28
+ 2025-04-03 01:12:36,824 - INFO - Train Epoch: 3 [0/42 (0%)] Loss: 3.8934
29
+ 2025-04-03 01:12:44,441 - INFO - Train Epoch: 3 [10/42 (24%)] Loss: 3.7839
30
+ 2025-04-03 01:12:52,069 - INFO - Train Epoch: 3 [20/42 (48%)] Loss: 3.8145
31
+ 2025-04-03 01:12:59,693 - INFO - Train Epoch: 3 [30/42 (71%)] Loss: 3.7579
32
+ 2025-04-03 01:13:07,337 - INFO - Train Epoch: 3 [40/42 (95%)] Loss: 3.7666
33
+ 2025-04-03 01:13:07,752 - INFO - Epoch 3 - Train Loss: 3.8417
34
+ 2025-04-03 01:13:30,187 - INFO - Epoch 3 - Validation Accuracy: 9.27%
35
+ 2025-04-03 01:13:30,188 - INFO - New best validation accuracy: 9.27%
36
+ 2025-04-03 01:13:31,355 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
37
+ 2025-04-03 01:13:32,590 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
38
+ 2025-04-03 01:13:32,590 - INFO - Epoch 4/20
39
+ 2025-04-03 01:13:34,199 - INFO - Train Epoch: 4 [0/42 (0%)] Loss: 3.8777
40
+ 2025-04-03 01:13:41,817 - INFO - Train Epoch: 4 [10/42 (24%)] Loss: 3.7311
41
+ 2025-04-03 01:13:49,438 - INFO - Train Epoch: 4 [20/42 (48%)] Loss: 3.7443
42
+ 2025-04-03 01:13:57,081 - INFO - Train Epoch: 4 [30/42 (71%)] Loss: 3.6386
43
+ 2025-04-03 01:14:04,710 - INFO - Train Epoch: 4 [40/42 (95%)] Loss: 3.6008
44
+ 2025-04-03 01:14:05,122 - INFO - Epoch 4 - Train Loss: 3.6972
45
+ 2025-04-03 01:14:27,345 - INFO - Epoch 4 - Validation Accuracy: 10.65%
46
+ 2025-04-03 01:14:27,346 - INFO - New best validation accuracy: 10.65%
47
+ 2025-04-03 01:14:28,633 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
48
+ 2025-04-03 01:14:29,874 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
49
+ 2025-04-03 01:14:29,874 - INFO - Epoch 5/20
50
+ 2025-04-03 01:14:31,517 - INFO - Train Epoch: 5 [0/42 (0%)] Loss: 3.5485
51
+ 2025-04-03 01:14:39,149 - INFO - Train Epoch: 5 [10/42 (24%)] Loss: 3.7418
52
+ 2025-04-03 01:14:46,765 - INFO - Train Epoch: 5 [20/42 (48%)] Loss: 3.5738
53
+ 2025-04-03 01:14:54,387 - INFO - Train Epoch: 5 [30/42 (71%)] Loss: 3.5214
54
+ 2025-04-03 01:15:02,011 - INFO - Train Epoch: 5 [40/42 (95%)] Loss: 3.5233
55
+ 2025-04-03 01:15:02,428 - INFO - Epoch 5 - Train Loss: 3.5759
56
+ 2025-04-03 01:15:25,279 - INFO - Epoch 5 - Validation Accuracy: 12.11%
57
+ 2025-04-03 01:15:25,279 - INFO - New best validation accuracy: 12.11%
58
+ 2025-04-03 01:15:26,592 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
59
+ 2025-04-03 01:15:27,766 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
60
+ 2025-04-03 01:15:27,766 - INFO - Epoch 6/20
61
+ 2025-04-03 01:15:29,432 - INFO - Train Epoch: 6 [0/42 (0%)] Loss: 3.5468
62
+ 2025-04-03 01:15:37,025 - INFO - Train Epoch: 6 [10/42 (24%)] Loss: 3.4507
63
+ 2025-04-03 01:15:44,646 - INFO - Train Epoch: 6 [20/42 (48%)] Loss: 3.4291
64
+ 2025-04-03 01:15:52,289 - INFO - Train Epoch: 6 [30/42 (71%)] Loss: 3.4125
65
+ 2025-04-03 01:15:59,926 - INFO - Train Epoch: 6 [40/42 (95%)] Loss: 3.5163
66
+ 2025-04-03 01:16:00,345 - INFO - Epoch 6 - Train Loss: 3.4742
67
+ 2025-04-03 01:16:22,785 - INFO - Epoch 6 - Validation Accuracy: 13.54%
68
+ 2025-04-03 01:16:22,785 - INFO - New best validation accuracy: 13.54%
69
+ 2025-04-03 01:16:24,031 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
70
+ 2025-04-03 01:16:25,243 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
71
+ 2025-04-03 01:16:25,243 - INFO - Epoch 7/20
72
+ 2025-04-03 01:16:26,926 - INFO - Train Epoch: 7 [0/42 (0%)] Loss: 3.4012
73
+ 2025-04-03 01:16:34,542 - INFO - Train Epoch: 7 [10/42 (24%)] Loss: 3.5195
74
+ 2025-04-03 01:16:42,166 - INFO - Train Epoch: 7 [20/42 (48%)] Loss: 3.4191
75
+ 2025-04-03 01:16:49,777 - INFO - Train Epoch: 7 [30/42 (71%)] Loss: 3.3120
76
+ 2025-04-03 01:16:57,410 - INFO - Train Epoch: 7 [40/42 (95%)] Loss: 3.3477
77
+ 2025-04-03 01:16:57,828 - INFO - Epoch 7 - Train Loss: 3.3847
78
+ 2025-04-03 01:17:20,211 - INFO - Epoch 7 - Validation Accuracy: 17.74%
79
+ 2025-04-03 01:17:20,211 - INFO - New best validation accuracy: 17.74%
80
+ 2025-04-03 01:17:21,421 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
81
+ 2025-04-03 01:17:22,630 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
82
+ 2025-04-03 01:17:22,630 - INFO - Epoch 8/20
83
+ 2025-04-03 01:17:24,278 - INFO - Train Epoch: 8 [0/42 (0%)] Loss: 3.3037
84
+ 2025-04-03 01:17:31,906 - INFO - Train Epoch: 8 [10/42 (24%)] Loss: 3.2954
85
+ 2025-04-03 01:17:39,524 - INFO - Train Epoch: 8 [20/42 (48%)] Loss: 3.2846
86
+ 2025-04-03 01:17:47,130 - INFO - Train Epoch: 8 [30/42 (71%)] Loss: 3.2343
87
+ 2025-04-03 01:17:54,757 - INFO - Train Epoch: 8 [40/42 (95%)] Loss: 3.2319
88
+ 2025-04-03 01:17:55,180 - INFO - Epoch 8 - Train Loss: 3.2934
89
+ 2025-04-03 01:18:17,698 - INFO - Epoch 8 - Validation Accuracy: 23.76%
90
+ 2025-04-03 01:18:17,698 - INFO - New best validation accuracy: 23.76%
91
+ 2025-04-03 01:18:18,823 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
92
+ 2025-04-03 01:18:20,000 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
93
+ 2025-04-03 01:18:20,000 - INFO - Epoch 9/20
94
+ 2025-04-03 01:18:21,636 - INFO - Train Epoch: 9 [0/42 (0%)] Loss: 3.3204
95
+ 2025-04-03 01:18:29,254 - INFO - Train Epoch: 9 [10/42 (24%)] Loss: 3.2838
96
+ 2025-04-03 01:18:36,878 - INFO - Train Epoch: 9 [20/42 (48%)] Loss: 3.1681
97
+ 2025-04-03 01:18:44,512 - INFO - Train Epoch: 9 [30/42 (71%)] Loss: 3.3006
98
+ 2025-04-03 01:18:52,112 - INFO - Train Epoch: 9 [40/42 (95%)] Loss: 3.1585
99
+ 2025-04-03 01:18:52,523 - INFO - Epoch 9 - Train Loss: 3.1982
100
+ 2025-04-03 01:19:15,008 - INFO - Epoch 9 - Validation Accuracy: 20.92%
101
+ 2025-04-03 01:19:16,259 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
102
+ 2025-04-03 01:19:16,259 - INFO - Epoch 10/20
103
+ 2025-04-03 01:19:17,882 - INFO - Train Epoch: 10 [0/42 (0%)] Loss: 3.1327
104
+ 2025-04-03 01:19:25,498 - INFO - Train Epoch: 10 [10/42 (24%)] Loss: 3.1187
105
+ 2025-04-03 01:19:33,157 - INFO - Train Epoch: 10 [20/42 (48%)] Loss: 3.1088
106
+ 2025-04-03 01:19:40,807 - INFO - Train Epoch: 10 [30/42 (71%)] Loss: 3.0296
107
+ 2025-04-03 01:19:48,456 - INFO - Train Epoch: 10 [40/42 (95%)] Loss: 3.0493
108
+ 2025-04-03 01:19:48,876 - INFO - Epoch 10 - Train Loss: 3.1154
109
+ 2025-04-03 01:20:11,185 - INFO - Epoch 10 - Validation Accuracy: 29.06%
110
+ 2025-04-03 01:20:11,185 - INFO - New best validation accuracy: 29.06%
111
+ 2025-04-03 01:20:13,475 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
112
+ 2025-04-03 01:20:15,114 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
113
+ 2025-04-03 01:20:15,114 - INFO - Epoch 11/20
114
+ 2025-04-03 01:20:16,806 - INFO - Train Epoch: 11 [0/42 (0%)] Loss: 3.0593
115
+ 2025-04-03 01:20:24,450 - INFO - Train Epoch: 11 [10/42 (24%)] Loss: 2.9980
116
+ 2025-04-03 01:20:32,085 - INFO - Train Epoch: 11 [20/42 (48%)] Loss: 3.0606
117
+ 2025-04-03 01:20:39,721 - INFO - Train Epoch: 11 [30/42 (71%)] Loss: 3.1429
118
+ 2025-04-03 01:20:47,360 - INFO - Train Epoch: 11 [40/42 (95%)] Loss: 2.9749
119
+ 2025-04-03 01:20:47,774 - INFO - Epoch 11 - Train Loss: 3.0394
120
+ 2025-04-03 01:21:10,082 - INFO - Epoch 11 - Validation Accuracy: 29.52%
121
+ 2025-04-03 01:21:10,082 - INFO - New best validation accuracy: 29.52%
122
+ 2025-04-03 01:21:11,279 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/best_model
123
+ 2025-04-03 01:21:12,510 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
124
+ 2025-04-03 01:21:12,510 - INFO - Epoch 12/20
125
+ 2025-04-03 01:21:14,171 - INFO - Train Epoch: 12 [0/42 (0%)] Loss: 3.0459
126
+ 2025-04-03 01:21:21,836 - INFO - Train Epoch: 12 [10/42 (24%)] Loss: 2.9567
127
+ 2025-04-03 01:21:29,504 - INFO - Train Epoch: 12 [20/42 (48%)] Loss: 2.9509
128
+ 2025-04-03 01:21:37,184 - INFO - Train Epoch: 12 [30/42 (71%)] Loss: 2.8792
129
+ 2025-04-03 01:21:44,878 - INFO - Train Epoch: 12 [40/42 (95%)] Loss: 2.9624
130
+ 2025-04-03 01:21:45,305 - INFO - Epoch 12 - Train Loss: 2.9771
131
+ 2025-04-03 01:22:08,095 - INFO - Epoch 12 - Validation Accuracy: 28.67%
132
+ 2025-04-03 01:22:09,375 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
133
+ 2025-04-03 01:22:09,375 - INFO - Epoch 13/20
134
+ 2025-04-03 01:22:11,023 - INFO - Train Epoch: 13 [0/42 (0%)] Loss: 2.8965
135
+ 2025-04-03 01:22:18,661 - INFO - Train Epoch: 13 [10/42 (24%)] Loss: 3.0182
136
+ 2025-04-03 01:22:26,289 - INFO - Train Epoch: 13 [20/42 (48%)] Loss: 2.8722
137
+ 2025-04-03 01:22:33,919 - INFO - Train Epoch: 13 [30/42 (71%)] Loss: 2.9651
138
+ 2025-04-03 01:22:41,509 - INFO - Train Epoch: 13 [40/42 (95%)] Loss: 2.7654
139
+ 2025-04-03 01:22:41,926 - INFO - Epoch 13 - Train Loss: 2.9228
140
+ 2025-04-03 01:23:04,348 - INFO - Epoch 13 - Validation Accuracy: 27.95%
141
+ 2025-04-03 01:23:05,604 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
142
+ 2025-04-03 01:23:05,604 - INFO - Epoch 14/20
143
+ 2025-04-03 01:23:07,273 - INFO - Train Epoch: 14 [0/42 (0%)] Loss: 2.8823
144
+ 2025-04-03 01:23:14,906 - INFO - Train Epoch: 14 [10/42 (24%)] Loss: 3.0117
145
+ 2025-04-03 01:23:22,529 - INFO - Train Epoch: 14 [20/42 (48%)] Loss: 2.8088
146
+ 2025-04-03 01:23:30,143 - INFO - Train Epoch: 14 [30/42 (71%)] Loss: 2.7487
147
+ 2025-04-03 01:23:37,759 - INFO - Train Epoch: 14 [40/42 (95%)] Loss: 2.7790
148
+ 2025-04-03 01:23:38,180 - INFO - Epoch 14 - Train Loss: 2.8661
149
+ 2025-04-03 01:24:00,479 - INFO - Epoch 14 - Validation Accuracy: 29.26%
150
+ 2025-04-03 01:24:01,672 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
151
+ 2025-04-03 01:24:01,673 - INFO - Epoch 15/20
152
+ 2025-04-03 01:24:03,322 - INFO - Train Epoch: 15 [0/42 (0%)] Loss: 2.8640
153
+ 2025-04-03 01:24:11,081 - INFO - Train Epoch: 15 [10/42 (24%)] Loss: 2.8177
154
+ 2025-04-03 01:24:18,733 - INFO - Train Epoch: 15 [20/42 (48%)] Loss: 2.8138
155
+ 2025-04-03 01:24:26,356 - INFO - Train Epoch: 15 [30/42 (71%)] Loss: 2.7692
156
+ 2025-04-03 01:24:33,993 - INFO - Train Epoch: 15 [40/42 (95%)] Loss: 2.8406
157
+ 2025-04-03 01:24:34,409 - INFO - Epoch 15 - Train Loss: 2.8136
158
+ 2025-04-03 01:24:56,938 - INFO - Epoch 15 - Validation Accuracy: 27.76%
159
+ 2025-04-03 01:24:58,184 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
160
+ 2025-04-03 01:24:58,184 - INFO - Epoch 16/20
161
+ 2025-04-03 01:24:59,819 - INFO - Train Epoch: 16 [0/42 (0%)] Loss: 2.7583
162
+ 2025-04-03 01:25:07,452 - INFO - Train Epoch: 16 [10/42 (24%)] Loss: 2.8620
163
+ 2025-04-03 01:25:15,057 - INFO - Train Epoch: 16 [20/42 (48%)] Loss: 2.6715
164
+ 2025-04-03 01:25:22,661 - INFO - Train Epoch: 16 [30/42 (71%)] Loss: 2.7681
165
+ 2025-04-03 01:25:30,273 - INFO - Train Epoch: 16 [40/42 (95%)] Loss: 2.7212
166
+ 2025-04-03 01:25:30,699 - INFO - Epoch 16 - Train Loss: 2.7621
167
+ 2025-04-03 01:25:52,957 - INFO - Epoch 16 - Validation Accuracy: 28.21%
168
+ 2025-04-03 01:25:54,175 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
169
+ 2025-04-03 01:25:54,175 - INFO - Epoch 17/20
170
+ 2025-04-03 01:25:55,823 - INFO - Train Epoch: 17 [0/42 (0%)] Loss: 2.6676
171
+ 2025-04-03 01:26:03,437 - INFO - Train Epoch: 17 [10/42 (24%)] Loss: 2.6322
172
+ 2025-04-03 01:26:11,050 - INFO - Train Epoch: 17 [20/42 (48%)] Loss: 2.6923
173
+ 2025-04-03 01:26:18,689 - INFO - Train Epoch: 17 [30/42 (71%)] Loss: 2.7568
174
+ 2025-04-03 01:26:26,328 - INFO - Train Epoch: 17 [40/42 (95%)] Loss: 2.6752
175
+ 2025-04-03 01:26:26,740 - INFO - Epoch 17 - Train Loss: 2.7106
176
+ 2025-04-03 01:26:49,563 - INFO - Epoch 17 - Validation Accuracy: 29.00%
177
+ 2025-04-03 01:26:50,787 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
178
+ 2025-04-03 01:26:50,787 - INFO - Epoch 18/20
179
+ 2025-04-03 01:26:52,453 - INFO - Train Epoch: 18 [0/42 (0%)] Loss: 2.6312
180
+ 2025-04-03 01:27:00,175 - INFO - Train Epoch: 18 [10/42 (24%)] Loss: 2.6472
181
+ 2025-04-03 01:27:07,894 - INFO - Train Epoch: 18 [20/42 (48%)] Loss: 2.6186
182
+ 2025-04-03 01:27:15,563 - INFO - Train Epoch: 18 [30/42 (71%)] Loss: 2.6047
183
+ 2025-04-03 01:27:23,165 - INFO - Train Epoch: 18 [40/42 (95%)] Loss: 2.7139
184
+ 2025-04-03 01:27:23,582 - INFO - Epoch 18 - Train Loss: 2.6784
185
+ 2025-04-03 01:27:45,976 - INFO - Epoch 18 - Validation Accuracy: 28.50%
186
+ 2025-04-03 01:27:47,145 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
187
+ 2025-04-03 01:27:47,146 - INFO - Epoch 19/20
188
+ 2025-04-03 01:27:48,763 - INFO - Train Epoch: 19 [0/42 (0%)] Loss: 2.6626
189
+ 2025-04-03 01:27:56,392 - INFO - Train Epoch: 19 [10/42 (24%)] Loss: 2.7262
190
+ 2025-04-03 01:28:04,036 - INFO - Train Epoch: 19 [20/42 (48%)] Loss: 2.5900
191
+ 2025-04-03 01:28:11,683 - INFO - Train Epoch: 19 [30/42 (71%)] Loss: 2.5490
192
+ 2025-04-03 01:28:19,363 - INFO - Train Epoch: 19 [40/42 (95%)] Loss: 2.6195
193
+ 2025-04-03 01:28:19,783 - INFO - Epoch 19 - Train Loss: 2.6453
194
+ 2025-04-03 01:28:42,858 - INFO - Epoch 19 - Validation Accuracy: 29.27%
195
+ 2025-04-03 01:28:44,172 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
196
+ 2025-04-03 01:28:44,172 - INFO - Epoch 20/20
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+ 2025-04-03 01:28:45,828 - INFO - Train Epoch: 20 [0/42 (0%)] Loss: 2.6073
198
+ 2025-04-03 01:28:53,426 - INFO - Train Epoch: 20 [10/42 (24%)] Loss: 2.7481
199
+ 2025-04-03 01:29:01,060 - INFO - Train Epoch: 20 [20/42 (48%)] Loss: 2.6463
200
+ 2025-04-03 01:29:08,700 - INFO - Train Epoch: 20 [30/42 (71%)] Loss: 2.6524
201
+ 2025-04-03 01:29:16,342 - INFO - Train Epoch: 20 [40/42 (95%)] Loss: 2.5949
202
+ 2025-04-03 01:29:16,771 - INFO - Epoch 20 - Train Loss: 2.6154
203
+ 2025-04-03 01:29:39,144 - INFO - Epoch 20 - Validation Accuracy: 29.03%
204
+ 2025-04-03 01:29:40,345 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
205
+ 2025-04-03 01:30:02,784 - INFO - Final validation accuracy: 29.03%
206
+ 2025-04-03 01:30:02,784 - INFO - Best validation accuracy: 29.52%
207
+ 2025-04-03 01:30:03,950 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_30_percent/checkpoints/checkpoint_last
208
+ 2025-04-03 01:30:03,950 - INFO - Training completed
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1
+ 2025-04-03 01:30:13,044 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=0.75, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='resnet', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=6), gpu=6, seed=42, num_workers=4, output_dir='./outputs/exp2/resnet/fewshot_75_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
2
+ 2025-04-03 01:30:13,045 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
3
+ 2025-04-03 01:30:44,329 - INFO - Initializing RobustMedClip model
4
+ 2025-04-03 01:30:50,802 - INFO - Starting training
5
+ 2025-04-03 01:30:50,802 - INFO - Epoch 1/20
6
+ 2025-04-03 01:30:52,788 - INFO - Train Epoch: 1 [0/104 (0%)] Loss: 4.4435
7
+ 2025-04-03 01:31:00,472 - INFO - Train Epoch: 1 [10/104 (10%)] Loss: 4.4472
8
+ 2025-04-03 01:31:08,189 - INFO - Train Epoch: 1 [20/104 (19%)] Loss: 4.3180
9
+ 2025-04-03 01:31:15,913 - INFO - Train Epoch: 1 [30/104 (29%)] Loss: 4.2816
10
+ 2025-04-03 01:31:23,592 - INFO - Train Epoch: 1 [40/104 (38%)] Loss: 4.1349
11
+ 2025-04-03 01:31:31,300 - INFO - Train Epoch: 1 [50/104 (48%)] Loss: 4.1235
12
+ 2025-04-03 01:31:38,976 - INFO - Train Epoch: 1 [60/104 (58%)] Loss: 3.9298
13
+ 2025-04-03 01:31:46,666 - INFO - Train Epoch: 1 [70/104 (67%)] Loss: 3.9198
14
+ 2025-04-03 01:31:54,355 - INFO - Train Epoch: 1 [80/104 (77%)] Loss: 3.8352
15
+ 2025-04-03 01:32:02,062 - INFO - Train Epoch: 1 [90/104 (87%)] Loss: 3.8732
16
+ 2025-04-03 01:32:09,749 - INFO - Train Epoch: 1 [100/104 (96%)] Loss: 3.9152
17
+ 2025-04-03 01:32:11,886 - INFO - Epoch 1 - Train Loss: 4.1058
18
+ 2025-04-03 01:32:34,818 - INFO - Epoch 1 - Validation Accuracy: 8.55%
19
+ 2025-04-03 01:32:34,818 - INFO - New best validation accuracy: 8.55%
20
+ 2025-04-03 01:32:36,407 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
21
+ 2025-04-03 01:32:37,618 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
22
+ 2025-04-03 01:32:37,618 - INFO - Epoch 2/20
23
+ 2025-04-03 01:32:39,278 - INFO - Train Epoch: 2 [0/104 (0%)] Loss: 3.8756
24
+ 2025-04-03 01:32:46,983 - INFO - Train Epoch: 2 [10/104 (10%)] Loss: 3.8692
25
+ 2025-04-03 01:32:54,664 - INFO - Train Epoch: 2 [20/104 (19%)] Loss: 3.7383
26
+ 2025-04-03 01:33:02,423 - INFO - Train Epoch: 2 [30/104 (29%)] Loss: 3.7109
27
+ 2025-04-03 01:33:10,147 - INFO - Train Epoch: 2 [40/104 (38%)] Loss: 3.7146
28
+ 2025-04-03 01:33:17,864 - INFO - Train Epoch: 2 [50/104 (48%)] Loss: 3.6841
29
+ 2025-04-03 01:33:25,559 - INFO - Train Epoch: 2 [60/104 (58%)] Loss: 3.5990
30
+ 2025-04-03 01:33:33,256 - INFO - Train Epoch: 2 [70/104 (67%)] Loss: 3.6181
31
+ 2025-04-03 01:33:40,945 - INFO - Train Epoch: 2 [80/104 (77%)] Loss: 3.6002
32
+ 2025-04-03 01:33:48,641 - INFO - Train Epoch: 2 [90/104 (87%)] Loss: 3.5869
33
+ 2025-04-03 01:33:56,350 - INFO - Train Epoch: 2 [100/104 (96%)] Loss: 3.5065
34
+ 2025-04-03 01:33:58,459 - INFO - Epoch 2 - Train Loss: 3.6736
35
+ 2025-04-03 01:34:21,412 - INFO - Epoch 2 - Validation Accuracy: 12.04%
36
+ 2025-04-03 01:34:21,412 - INFO - New best validation accuracy: 12.04%
37
+ 2025-04-03 01:34:23,194 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
38
+ 2025-04-03 01:34:24,454 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
39
+ 2025-04-03 01:34:24,454 - INFO - Epoch 3/20
40
+ 2025-04-03 01:34:26,264 - INFO - Train Epoch: 3 [0/104 (0%)] Loss: 3.5557
41
+ 2025-04-03 01:34:33,926 - INFO - Train Epoch: 3 [10/104 (10%)] Loss: 3.5258
42
+ 2025-04-03 01:34:41,590 - INFO - Train Epoch: 3 [20/104 (19%)] Loss: 3.4864
43
+ 2025-04-03 01:34:49,323 - INFO - Train Epoch: 3 [30/104 (29%)] Loss: 3.5381
44
+ 2025-04-03 01:34:57,092 - INFO - Train Epoch: 3 [40/104 (38%)] Loss: 3.4952
45
+ 2025-04-03 01:35:04,809 - INFO - Train Epoch: 3 [50/104 (48%)] Loss: 3.4272
46
+ 2025-04-03 01:35:12,543 - INFO - Train Epoch: 3 [60/104 (58%)] Loss: 3.4048
47
+ 2025-04-03 01:35:20,228 - INFO - Train Epoch: 3 [70/104 (67%)] Loss: 3.3689
48
+ 2025-04-03 01:35:27,912 - INFO - Train Epoch: 3 [80/104 (77%)] Loss: 3.3465
49
+ 2025-04-03 01:35:35,594 - INFO - Train Epoch: 3 [90/104 (87%)] Loss: 3.3744
50
+ 2025-04-03 01:35:43,282 - INFO - Train Epoch: 3 [100/104 (96%)] Loss: 3.2145
51
+ 2025-04-03 01:35:45,385 - INFO - Epoch 3 - Train Loss: 3.4161
52
+ 2025-04-03 01:36:08,106 - INFO - Epoch 3 - Validation Accuracy: 21.06%
53
+ 2025-04-03 01:36:08,106 - INFO - New best validation accuracy: 21.06%
54
+ 2025-04-03 01:36:09,897 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
55
+ 2025-04-03 01:36:11,908 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
56
+ 2025-04-03 01:36:11,909 - INFO - Epoch 4/20
57
+ 2025-04-03 01:36:13,586 - INFO - Train Epoch: 4 [0/104 (0%)] Loss: 3.2246
58
+ 2025-04-03 01:36:21,260 - INFO - Train Epoch: 4 [10/104 (10%)] Loss: 3.2907
59
+ 2025-04-03 01:36:28,946 - INFO - Train Epoch: 4 [20/104 (19%)] Loss: 3.3580
60
+ 2025-04-03 01:36:36,775 - INFO - Train Epoch: 4 [30/104 (29%)] Loss: 3.2649
61
+ 2025-04-03 01:36:44,521 - INFO - Train Epoch: 4 [40/104 (38%)] Loss: 3.1972
62
+ 2025-04-03 01:36:52,237 - INFO - Train Epoch: 4 [50/104 (48%)] Loss: 3.2105
63
+ 2025-04-03 01:36:59,994 - INFO - Train Epoch: 4 [60/104 (58%)] Loss: 3.2121
64
+ 2025-04-03 01:37:07,685 - INFO - Train Epoch: 4 [70/104 (67%)] Loss: 3.0754
65
+ 2025-04-03 01:37:15,380 - INFO - Train Epoch: 4 [80/104 (77%)] Loss: 3.1332
66
+ 2025-04-03 01:37:23,058 - INFO - Train Epoch: 4 [90/104 (87%)] Loss: 3.1055
67
+ 2025-04-03 01:37:30,773 - INFO - Train Epoch: 4 [100/104 (96%)] Loss: 3.0453
68
+ 2025-04-03 01:37:32,905 - INFO - Epoch 4 - Train Loss: 3.1930
69
+ 2025-04-03 01:37:55,581 - INFO - Epoch 4 - Validation Accuracy: 27.82%
70
+ 2025-04-03 01:37:55,581 - INFO - New best validation accuracy: 27.82%
71
+ 2025-04-03 01:37:57,343 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
72
+ 2025-04-03 01:37:58,573 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
73
+ 2025-04-03 01:37:58,574 - INFO - Epoch 5/20
74
+ 2025-04-03 01:38:00,271 - INFO - Train Epoch: 5 [0/104 (0%)] Loss: 3.0900
75
+ 2025-04-03 01:38:08,015 - INFO - Train Epoch: 5 [10/104 (10%)] Loss: 3.0084
76
+ 2025-04-03 01:38:15,706 - INFO - Train Epoch: 5 [20/104 (19%)] Loss: 3.0502
77
+ 2025-04-03 01:38:23,404 - INFO - Train Epoch: 5 [30/104 (29%)] Loss: 2.9709
78
+ 2025-04-03 01:38:31,073 - INFO - Train Epoch: 5 [40/104 (38%)] Loss: 3.0550
79
+ 2025-04-03 01:38:38,772 - INFO - Train Epoch: 5 [50/104 (48%)] Loss: 3.0306
80
+ 2025-04-03 01:38:46,472 - INFO - Train Epoch: 5 [60/104 (58%)] Loss: 3.0669
81
+ 2025-04-03 01:38:54,222 - INFO - Train Epoch: 5 [70/104 (67%)] Loss: 3.1172
82
+ 2025-04-03 01:39:01,925 - INFO - Train Epoch: 5 [80/104 (77%)] Loss: 2.9203
83
+ 2025-04-03 01:39:09,616 - INFO - Train Epoch: 5 [90/104 (87%)] Loss: 2.8900
84
+ 2025-04-03 01:39:17,293 - INFO - Train Epoch: 5 [100/104 (96%)] Loss: 2.9681
85
+ 2025-04-03 01:39:19,394 - INFO - Epoch 5 - Train Loss: 2.9974
86
+ 2025-04-03 01:39:41,993 - INFO - Epoch 5 - Validation Accuracy: 30.09%
87
+ 2025-04-03 01:39:41,994 - INFO - New best validation accuracy: 30.09%
88
+ 2025-04-03 01:39:43,680 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
89
+ 2025-04-03 01:39:44,924 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
90
+ 2025-04-03 01:39:44,924 - INFO - Epoch 6/20
91
+ 2025-04-03 01:39:46,599 - INFO - Train Epoch: 6 [0/104 (0%)] Loss: 2.9372
92
+ 2025-04-03 01:39:54,278 - INFO - Train Epoch: 6 [10/104 (10%)] Loss: 2.8977
93
+ 2025-04-03 01:40:01,946 - INFO - Train Epoch: 6 [20/104 (19%)] Loss: 2.9976
94
+ 2025-04-03 01:40:09,679 - INFO - Train Epoch: 6 [30/104 (29%)] Loss: 2.8925
95
+ 2025-04-03 01:40:17,454 - INFO - Train Epoch: 6 [40/104 (38%)] Loss: 2.8012
96
+ 2025-04-03 01:40:25,130 - INFO - Train Epoch: 6 [50/104 (48%)] Loss: 2.8586
97
+ 2025-04-03 01:40:32,802 - INFO - Train Epoch: 6 [60/104 (58%)] Loss: 2.8159
98
+ 2025-04-03 01:40:40,545 - INFO - Train Epoch: 6 [70/104 (67%)] Loss: 2.7961
99
+ 2025-04-03 01:40:48,282 - INFO - Train Epoch: 6 [80/104 (77%)] Loss: 2.7952
100
+ 2025-04-03 01:40:56,066 - INFO - Train Epoch: 6 [90/104 (87%)] Loss: 2.8781
101
+ 2025-04-03 01:41:03,771 - INFO - Train Epoch: 6 [100/104 (96%)] Loss: 2.7466
102
+ 2025-04-03 01:41:05,884 - INFO - Epoch 6 - Train Loss: 2.8548
103
+ 2025-04-03 01:41:28,684 - INFO - Epoch 6 - Validation Accuracy: 28.39%
104
+ 2025-04-03 01:41:30,781 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:41:30,782 - INFO - Epoch 7/20
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+ 2025-04-03 01:41:32,486 - INFO - Train Epoch: 7 [0/104 (0%)] Loss: 2.8413
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+ 2025-04-03 01:41:40,156 - INFO - Train Epoch: 7 [10/104 (10%)] Loss: 2.7934
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+ 2025-04-03 01:41:47,822 - INFO - Train Epoch: 7 [20/104 (19%)] Loss: 2.6438
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+ 2025-04-03 01:41:55,486 - INFO - Train Epoch: 7 [30/104 (29%)] Loss: 2.6843
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+ 2025-04-03 01:42:03,184 - INFO - Train Epoch: 7 [40/104 (38%)] Loss: 2.8012
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+ 2025-04-03 01:42:10,864 - INFO - Train Epoch: 7 [50/104 (48%)] Loss: 2.7166
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+ 2025-04-03 01:42:18,538 - INFO - Train Epoch: 7 [60/104 (58%)] Loss: 2.6977
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+ 2025-04-03 01:42:26,204 - INFO - Train Epoch: 7 [70/104 (67%)] Loss: 2.7297
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+ 2025-04-03 01:42:33,876 - INFO - Train Epoch: 7 [80/104 (77%)] Loss: 2.6165
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+ 2025-04-03 01:42:41,551 - INFO - Train Epoch: 7 [90/104 (87%)] Loss: 2.6980
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+ 2025-04-03 01:42:49,241 - INFO - Train Epoch: 7 [100/104 (96%)] Loss: 2.6096
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+ 2025-04-03 01:42:51,359 - INFO - Epoch 7 - Train Loss: 2.7299
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+ 2025-04-03 01:43:14,132 - INFO - Epoch 7 - Validation Accuracy: 28.47%
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+ 2025-04-03 01:43:15,416 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:43:15,416 - INFO - Epoch 8/20
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+ 2025-04-03 01:43:17,077 - INFO - Train Epoch: 8 [0/104 (0%)] Loss: 2.6848
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+ 2025-04-03 01:43:24,747 - INFO - Train Epoch: 8 [10/104 (10%)] Loss: 2.6605
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+ 2025-04-03 01:43:32,423 - INFO - Train Epoch: 8 [20/104 (19%)] Loss: 2.6695
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+ 2025-04-03 01:43:40,103 - INFO - Train Epoch: 8 [30/104 (29%)] Loss: 2.6677
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+ 2025-04-03 01:43:47,770 - INFO - Train Epoch: 8 [40/104 (38%)] Loss: 2.5701
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+ 2025-04-03 01:43:55,456 - INFO - Train Epoch: 8 [50/104 (48%)] Loss: 2.6838
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+ 2025-04-03 01:44:03,190 - INFO - Train Epoch: 8 [60/104 (58%)] Loss: 2.6700
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+ 2025-04-03 01:44:10,874 - INFO - Train Epoch: 8 [70/104 (67%)] Loss: 2.5827
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+ 2025-04-03 01:44:18,555 - INFO - Train Epoch: 8 [80/104 (77%)] Loss: 2.5898
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+ 2025-04-03 01:44:26,227 - INFO - Train Epoch: 8 [90/104 (87%)] Loss: 2.6210
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+ 2025-04-03 01:44:33,940 - INFO - Train Epoch: 8 [100/104 (96%)] Loss: 2.6339
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+ 2025-04-03 01:44:36,053 - INFO - Epoch 8 - Train Loss: 2.6448
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+ 2025-04-03 01:44:58,879 - INFO - Epoch 8 - Validation Accuracy: 29.73%
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+ 2025-04-03 01:45:00,124 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:45:00,124 - INFO - Epoch 9/20
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+ 2025-04-03 01:45:01,851 - INFO - Train Epoch: 9 [0/104 (0%)] Loss: 2.5902
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+ 2025-04-03 01:45:09,599 - INFO - Train Epoch: 9 [10/104 (10%)] Loss: 2.5626
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+ 2025-04-03 01:45:17,281 - INFO - Train Epoch: 9 [20/104 (19%)] Loss: 2.6072
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+ 2025-04-03 01:45:24,977 - INFO - Train Epoch: 9 [30/104 (29%)] Loss: 2.5895
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+ 2025-04-03 01:45:32,666 - INFO - Train Epoch: 9 [40/104 (38%)] Loss: 2.5000
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+ 2025-04-03 01:45:40,374 - INFO - Train Epoch: 9 [50/104 (48%)] Loss: 2.7391
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+ 2025-04-03 01:45:48,066 - INFO - Train Epoch: 9 [60/104 (58%)] Loss: 2.6057
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+ 2025-04-03 01:45:55,769 - INFO - Train Epoch: 9 [70/104 (67%)] Loss: 2.5561
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+ 2025-04-03 01:46:03,496 - INFO - Train Epoch: 9 [80/104 (77%)] Loss: 2.6011
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+ 2025-04-03 01:46:11,185 - INFO - Train Epoch: 9 [90/104 (87%)] Loss: 2.6279
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+ 2025-04-03 01:46:18,854 - INFO - Train Epoch: 9 [100/104 (96%)] Loss: 2.7456
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+ 2025-04-03 01:46:20,968 - INFO - Epoch 9 - Train Loss: 2.5792
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+ 2025-04-03 01:46:43,872 - INFO - Epoch 9 - Validation Accuracy: 31.29%
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+ 2025-04-03 01:46:43,872 - INFO - New best validation accuracy: 31.29%
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+ 2025-04-03 01:46:45,701 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
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+ 2025-04-03 01:46:46,970 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:46:46,970 - INFO - Epoch 10/20
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+ 2025-04-03 01:46:48,717 - INFO - Train Epoch: 10 [0/104 (0%)] Loss: 2.4719
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+ 2025-04-03 01:46:56,420 - INFO - Train Epoch: 10 [10/104 (10%)] Loss: 2.5787
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+ 2025-04-03 01:47:04,081 - INFO - Train Epoch: 10 [20/104 (19%)] Loss: 2.4371
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+ 2025-04-03 01:47:11,779 - INFO - Train Epoch: 10 [30/104 (29%)] Loss: 2.5231
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+ 2025-04-03 01:47:19,454 - INFO - Train Epoch: 10 [40/104 (38%)] Loss: 2.5061
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+ 2025-04-03 01:47:27,160 - INFO - Train Epoch: 10 [50/104 (48%)] Loss: 2.4925
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+ 2025-04-03 01:47:34,839 - INFO - Train Epoch: 10 [60/104 (58%)] Loss: 2.5309
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+ 2025-04-03 01:47:42,513 - INFO - Train Epoch: 10 [70/104 (67%)] Loss: 2.5260
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+ 2025-04-03 01:47:50,182 - INFO - Train Epoch: 10 [80/104 (77%)] Loss: 2.4322
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+ 2025-04-03 01:47:57,912 - INFO - Train Epoch: 10 [90/104 (87%)] Loss: 2.4387
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+ 2025-04-03 01:48:05,644 - INFO - Train Epoch: 10 [100/104 (96%)] Loss: 2.4094
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+ 2025-04-03 01:48:07,768 - INFO - Epoch 10 - Train Loss: 2.5240
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+ 2025-04-03 01:48:30,580 - INFO - Epoch 10 - Validation Accuracy: 30.93%
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+ 2025-04-03 01:48:31,929 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:48:31,930 - INFO - Epoch 11/20
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+ 2025-04-03 01:48:33,608 - INFO - Train Epoch: 11 [0/104 (0%)] Loss: 2.5692
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+ 2025-04-03 01:48:41,348 - INFO - Train Epoch: 11 [10/104 (10%)] Loss: 2.4728
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+ 2025-04-03 01:48:49,080 - INFO - Train Epoch: 11 [20/104 (19%)] Loss: 2.4597
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+ 2025-04-03 01:48:56,797 - INFO - Train Epoch: 11 [30/104 (29%)] Loss: 2.5762
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+ 2025-04-03 01:49:04,468 - INFO - Train Epoch: 11 [40/104 (38%)] Loss: 2.4930
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+ 2025-04-03 01:49:12,156 - INFO - Train Epoch: 11 [50/104 (48%)] Loss: 2.5325
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+ 2025-04-03 01:49:19,822 - INFO - Train Epoch: 11 [60/104 (58%)] Loss: 2.4996
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+ 2025-04-03 01:49:27,489 - INFO - Train Epoch: 11 [70/104 (67%)] Loss: 2.4077
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+ 2025-04-03 01:49:35,157 - INFO - Train Epoch: 11 [80/104 (77%)] Loss: 2.4551
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+ 2025-04-03 01:49:42,845 - INFO - Train Epoch: 11 [90/104 (87%)] Loss: 2.3971
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+ 2025-04-03 01:49:50,508 - INFO - Train Epoch: 11 [100/104 (96%)] Loss: 2.5312
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+ 2025-04-03 01:49:52,615 - INFO - Epoch 11 - Train Loss: 2.4718
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+ 2025-04-03 01:50:15,501 - INFO - Epoch 11 - Validation Accuracy: 32.72%
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+ 2025-04-03 01:50:15,501 - INFO - New best validation accuracy: 32.72%
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+ 2025-04-03 01:50:17,245 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
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+ 2025-04-03 01:50:18,514 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:50:18,515 - INFO - Epoch 12/20
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+ 2025-04-03 01:50:20,234 - INFO - Train Epoch: 12 [0/104 (0%)] Loss: 2.5144
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+ 2025-04-03 01:50:28,016 - INFO - Train Epoch: 12 [10/104 (10%)] Loss: 2.4459
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+ 2025-04-03 01:50:35,737 - INFO - Train Epoch: 12 [20/104 (19%)] Loss: 2.4437
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+ 2025-04-03 01:50:43,523 - INFO - Train Epoch: 12 [30/104 (29%)] Loss: 2.4039
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+ 2025-04-03 01:50:51,306 - INFO - Train Epoch: 12 [40/104 (38%)] Loss: 2.4261
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+ 2025-04-03 01:50:59,032 - INFO - Train Epoch: 12 [50/104 (48%)] Loss: 2.4846
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+ 2025-04-03 01:51:06,758 - INFO - Train Epoch: 12 [60/104 (58%)] Loss: 2.4268
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+ 2025-04-03 01:51:14,531 - INFO - Train Epoch: 12 [70/104 (67%)] Loss: 2.4076
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+ 2025-04-03 01:51:22,280 - INFO - Train Epoch: 12 [80/104 (77%)] Loss: 2.3471
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+ 2025-04-03 01:51:30,030 - INFO - Train Epoch: 12 [90/104 (87%)] Loss: 2.3849
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+ 2025-04-03 01:51:37,813 - INFO - Train Epoch: 12 [100/104 (96%)] Loss: 2.3860
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+ 2025-04-03 01:51:39,958 - INFO - Epoch 12 - Train Loss: 2.4124
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+ 2025-04-03 01:52:02,911 - INFO - Epoch 12 - Validation Accuracy: 31.50%
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+ 2025-04-03 01:52:04,326 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:52:04,326 - INFO - Epoch 13/20
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+ 2025-04-03 01:52:06,046 - INFO - Train Epoch: 13 [0/104 (0%)] Loss: 2.3783
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+ 2025-04-03 01:52:13,700 - INFO - Train Epoch: 13 [10/104 (10%)] Loss: 2.3805
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+ 2025-04-03 01:52:21,383 - INFO - Train Epoch: 13 [20/104 (19%)] Loss: 2.4060
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+ 2025-04-03 01:52:29,056 - INFO - Train Epoch: 13 [30/104 (29%)] Loss: 2.3811
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+ 2025-04-03 01:52:36,759 - INFO - Train Epoch: 13 [40/104 (38%)] Loss: 2.2339
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+ 2025-04-03 01:52:44,462 - INFO - Train Epoch: 13 [50/104 (48%)] Loss: 2.3760
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+ 2025-04-03 01:52:52,174 - INFO - Train Epoch: 13 [60/104 (58%)] Loss: 2.3151
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+ 2025-04-03 01:52:59,838 - INFO - Train Epoch: 13 [70/104 (67%)] Loss: 2.3144
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+ 2025-04-03 01:53:07,524 - INFO - Train Epoch: 13 [80/104 (77%)] Loss: 2.2688
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+ 2025-04-03 01:53:15,195 - INFO - Train Epoch: 13 [90/104 (87%)] Loss: 2.3977
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+ 2025-04-03 01:53:22,864 - INFO - Train Epoch: 13 [100/104 (96%)] Loss: 2.3075
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+ 2025-04-03 01:53:24,975 - INFO - Epoch 13 - Train Loss: 2.3424
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+ 2025-04-03 01:53:47,753 - INFO - Epoch 13 - Validation Accuracy: 37.31%
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+ 2025-04-03 01:53:47,753 - INFO - New best validation accuracy: 37.31%
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+ 2025-04-03 01:53:49,564 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
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+ 2025-04-03 01:53:50,853 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:53:50,854 - INFO - Epoch 14/20
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+ 2025-04-03 01:53:52,616 - INFO - Train Epoch: 14 [0/104 (0%)] Loss: 2.3217
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+ 2025-04-03 01:54:00,332 - INFO - Train Epoch: 14 [10/104 (10%)] Loss: 2.2728
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+ 2025-04-03 01:54:07,972 - INFO - Train Epoch: 14 [20/104 (19%)] Loss: 2.2707
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+ 2025-04-03 01:54:15,665 - INFO - Train Epoch: 14 [30/104 (29%)] Loss: 2.2242
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+ 2025-04-03 01:54:23,373 - INFO - Train Epoch: 14 [40/104 (38%)] Loss: 2.2611
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+ 2025-04-03 01:54:31,151 - INFO - Train Epoch: 14 [50/104 (48%)] Loss: 2.3379
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+ 2025-04-03 01:54:38,892 - INFO - Train Epoch: 14 [60/104 (58%)] Loss: 2.2423
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+ 2025-04-03 01:54:46,674 - INFO - Train Epoch: 14 [70/104 (67%)] Loss: 2.2690
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+ 2025-04-03 01:54:54,358 - INFO - Train Epoch: 14 [80/104 (77%)] Loss: 2.3688
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+ 2025-04-03 01:55:02,083 - INFO - Train Epoch: 14 [90/104 (87%)] Loss: 2.2661
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+ 2025-04-03 01:55:09,830 - INFO - Train Epoch: 14 [100/104 (96%)] Loss: 2.2277
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+ 2025-04-03 01:55:11,941 - INFO - Epoch 14 - Train Loss: 2.2844
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+ 2025-04-03 01:55:34,740 - INFO - Epoch 14 - Validation Accuracy: 35.10%
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+ 2025-04-03 01:55:36,053 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:55:36,053 - INFO - Epoch 15/20
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+ 2025-04-03 01:55:37,737 - INFO - Train Epoch: 15 [0/104 (0%)] Loss: 2.2810
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+ 2025-04-03 01:55:45,456 - INFO - Train Epoch: 15 [10/104 (10%)] Loss: 2.3266
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+ 2025-04-03 01:55:53,216 - INFO - Train Epoch: 15 [20/104 (19%)] Loss: 2.3232
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+ 2025-04-03 01:56:00,921 - INFO - Train Epoch: 15 [30/104 (29%)] Loss: 2.2326
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+ 2025-04-03 01:56:08,588 - INFO - Train Epoch: 15 [40/104 (38%)] Loss: 2.3419
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+ 2025-04-03 01:56:16,219 - INFO - Train Epoch: 15 [50/104 (48%)] Loss: 2.1927
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+ 2025-04-03 01:56:23,879 - INFO - Train Epoch: 15 [60/104 (58%)] Loss: 2.2193
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+ 2025-04-03 01:56:31,572 - INFO - Train Epoch: 15 [70/104 (67%)] Loss: 2.1818
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+ 2025-04-03 01:56:39,302 - INFO - Train Epoch: 15 [80/104 (77%)] Loss: 2.1799
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+ 2025-04-03 01:56:47,014 - INFO - Train Epoch: 15 [90/104 (87%)] Loss: 2.2162
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+ 2025-04-03 01:56:54,712 - INFO - Train Epoch: 15 [100/104 (96%)] Loss: 2.1453
243
+ 2025-04-03 01:56:56,842 - INFO - Epoch 15 - Train Loss: 2.2409
244
+ 2025-04-03 01:57:19,709 - INFO - Epoch 15 - Validation Accuracy: 36.55%
245
+ 2025-04-03 01:57:21,152 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:57:21,152 - INFO - Epoch 16/20
247
+ 2025-04-03 01:57:22,976 - INFO - Train Epoch: 16 [0/104 (0%)] Loss: 2.2429
248
+ 2025-04-03 01:57:30,650 - INFO - Train Epoch: 16 [10/104 (10%)] Loss: 2.2603
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+ 2025-04-03 01:57:38,324 - INFO - Train Epoch: 16 [20/104 (19%)] Loss: 2.2286
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+ 2025-04-03 01:57:45,997 - INFO - Train Epoch: 16 [30/104 (29%)] Loss: 2.1623
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+ 2025-04-03 01:57:53,669 - INFO - Train Epoch: 16 [40/104 (38%)] Loss: 2.2630
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+ 2025-04-03 01:58:01,340 - INFO - Train Epoch: 16 [50/104 (48%)] Loss: 2.1992
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+ 2025-04-03 01:58:09,045 - INFO - Train Epoch: 16 [60/104 (58%)] Loss: 2.2979
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+ 2025-04-03 01:58:16,730 - INFO - Train Epoch: 16 [70/104 (67%)] Loss: 2.2393
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+ 2025-04-03 01:58:24,414 - INFO - Train Epoch: 16 [80/104 (77%)] Loss: 2.2049
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+ 2025-04-03 01:58:32,119 - INFO - Train Epoch: 16 [90/104 (87%)] Loss: 2.1557
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+ 2025-04-03 01:58:39,792 - INFO - Train Epoch: 16 [100/104 (96%)] Loss: 2.0796
258
+ 2025-04-03 01:58:41,896 - INFO - Epoch 16 - Train Loss: 2.2155
259
+ 2025-04-03 01:59:04,543 - INFO - Epoch 16 - Validation Accuracy: 38.19%
260
+ 2025-04-03 01:59:04,543 - INFO - New best validation accuracy: 38.19%
261
+ 2025-04-03 01:59:06,234 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
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+ 2025-04-03 01:59:07,492 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 01:59:07,493 - INFO - Epoch 17/20
264
+ 2025-04-03 01:59:09,203 - INFO - Train Epoch: 17 [0/104 (0%)] Loss: 2.1706
265
+ 2025-04-03 01:59:16,867 - INFO - Train Epoch: 17 [10/104 (10%)] Loss: 2.1413
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+ 2025-04-03 01:59:24,538 - INFO - Train Epoch: 17 [20/104 (19%)] Loss: 2.1481
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+ 2025-04-03 01:59:32,207 - INFO - Train Epoch: 17 [30/104 (29%)] Loss: 2.2341
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+ 2025-04-03 01:59:39,882 - INFO - Train Epoch: 17 [40/104 (38%)] Loss: 2.1319
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+ 2025-04-03 01:59:47,563 - INFO - Train Epoch: 17 [50/104 (48%)] Loss: 2.0788
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+ 2025-04-03 01:59:55,235 - INFO - Train Epoch: 17 [60/104 (58%)] Loss: 2.3008
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+ 2025-04-03 02:00:02,907 - INFO - Train Epoch: 17 [70/104 (67%)] Loss: 2.0457
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+ 2025-04-03 02:00:10,587 - INFO - Train Epoch: 17 [80/104 (77%)] Loss: 2.2078
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+ 2025-04-03 02:00:18,272 - INFO - Train Epoch: 17 [90/104 (87%)] Loss: 2.2637
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+ 2025-04-03 02:00:25,975 - INFO - Train Epoch: 17 [100/104 (96%)] Loss: 2.2741
275
+ 2025-04-03 02:00:28,085 - INFO - Epoch 17 - Train Loss: 2.1837
276
+ 2025-04-03 02:00:50,940 - INFO - Epoch 17 - Validation Accuracy: 38.94%
277
+ 2025-04-03 02:00:50,940 - INFO - New best validation accuracy: 38.94%
278
+ 2025-04-03 02:00:52,576 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
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+ 2025-04-03 02:00:53,805 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
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+ 2025-04-03 02:00:53,805 - INFO - Epoch 18/20
281
+ 2025-04-03 02:00:55,503 - INFO - Train Epoch: 18 [0/104 (0%)] Loss: 2.3479
282
+ 2025-04-03 02:01:03,251 - INFO - Train Epoch: 18 [10/104 (10%)] Loss: 2.1366
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+ 2025-04-03 02:01:10,971 - INFO - Train Epoch: 18 [20/104 (19%)] Loss: 2.1164
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+ 2025-04-03 02:01:18,861 - INFO - Train Epoch: 18 [30/104 (29%)] Loss: 2.1832
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+ 2025-04-03 02:01:26,687 - INFO - Train Epoch: 18 [40/104 (38%)] Loss: 2.1303
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+ 2025-04-03 02:01:34,496 - INFO - Train Epoch: 18 [50/104 (48%)] Loss: 2.1415
287
+ 2025-04-03 02:01:42,226 - INFO - Train Epoch: 18 [60/104 (58%)] Loss: 2.2047
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+ 2025-04-03 02:01:50,041 - INFO - Train Epoch: 18 [70/104 (67%)] Loss: 2.1030
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+ 2025-04-03 02:01:57,793 - INFO - Train Epoch: 18 [80/104 (77%)] Loss: 2.1796
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+ 2025-04-03 02:02:05,607 - INFO - Train Epoch: 18 [90/104 (87%)] Loss: 2.0843
291
+ 2025-04-03 02:02:13,370 - INFO - Train Epoch: 18 [100/104 (96%)] Loss: 2.1812
292
+ 2025-04-03 02:02:15,509 - INFO - Epoch 18 - Train Loss: 2.1578
293
+ 2025-04-03 02:02:38,473 - INFO - Epoch 18 - Validation Accuracy: 40.03%
294
+ 2025-04-03 02:02:38,473 - INFO - New best validation accuracy: 40.03%
295
+ 2025-04-03 02:02:40,206 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
296
+ 2025-04-03 02:02:41,493 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
297
+ 2025-04-03 02:02:41,493 - INFO - Epoch 19/20
298
+ 2025-04-03 02:02:43,194 - INFO - Train Epoch: 19 [0/104 (0%)] Loss: 2.1390
299
+ 2025-04-03 02:02:50,961 - INFO - Train Epoch: 19 [10/104 (10%)] Loss: 2.1024
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+ 2025-04-03 02:02:58,654 - INFO - Train Epoch: 19 [20/104 (19%)] Loss: 2.2719
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+ 2025-04-03 02:03:06,386 - INFO - Train Epoch: 19 [30/104 (29%)] Loss: 2.0415
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+ 2025-04-03 02:03:14,136 - INFO - Train Epoch: 19 [40/104 (38%)] Loss: 2.1477
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+ 2025-04-03 02:03:21,882 - INFO - Train Epoch: 19 [50/104 (48%)] Loss: 2.1322
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+ 2025-04-03 02:03:29,605 - INFO - Train Epoch: 19 [60/104 (58%)] Loss: 2.1612
305
+ 2025-04-03 02:03:37,304 - INFO - Train Epoch: 19 [70/104 (67%)] Loss: 2.1194
306
+ 2025-04-03 02:03:45,016 - INFO - Train Epoch: 19 [80/104 (77%)] Loss: 2.0853
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+ 2025-04-03 02:03:52,732 - INFO - Train Epoch: 19 [90/104 (87%)] Loss: 2.0269
308
+ 2025-04-03 02:04:00,441 - INFO - Train Epoch: 19 [100/104 (96%)] Loss: 2.2318
309
+ 2025-04-03 02:04:02,575 - INFO - Epoch 19 - Train Loss: 2.1319
310
+ 2025-04-03 02:04:25,381 - INFO - Epoch 19 - Validation Accuracy: 41.74%
311
+ 2025-04-03 02:04:25,381 - INFO - New best validation accuracy: 41.74%
312
+ 2025-04-03 02:04:27,100 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/best_model
313
+ 2025-04-03 02:04:28,311 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
314
+ 2025-04-03 02:04:28,312 - INFO - Epoch 20/20
315
+ 2025-04-03 02:04:29,983 - INFO - Train Epoch: 20 [0/104 (0%)] Loss: 2.1354
316
+ 2025-04-03 02:04:37,745 - INFO - Train Epoch: 20 [10/104 (10%)] Loss: 2.1539
317
+ 2025-04-03 02:04:45,442 - INFO - Train Epoch: 20 [20/104 (19%)] Loss: 2.0385
318
+ 2025-04-03 02:04:53,178 - INFO - Train Epoch: 20 [30/104 (29%)] Loss: 2.0575
319
+ 2025-04-03 02:05:00,919 - INFO - Train Epoch: 20 [40/104 (38%)] Loss: 2.1162
320
+ 2025-04-03 02:05:08,711 - INFO - Train Epoch: 20 [50/104 (48%)] Loss: 2.1226
321
+ 2025-04-03 02:05:16,375 - INFO - Train Epoch: 20 [60/104 (58%)] Loss: 2.1580
322
+ 2025-04-03 02:05:24,094 - INFO - Train Epoch: 20 [70/104 (67%)] Loss: 2.1330
323
+ 2025-04-03 02:05:31,767 - INFO - Train Epoch: 20 [80/104 (77%)] Loss: 2.0756
324
+ 2025-04-03 02:05:39,540 - INFO - Train Epoch: 20 [90/104 (87%)] Loss: 2.1556
325
+ 2025-04-03 02:05:47,335 - INFO - Train Epoch: 20 [100/104 (96%)] Loss: 2.1004
326
+ 2025-04-03 02:05:49,468 - INFO - Epoch 20 - Train Loss: 2.1192
327
+ 2025-04-03 02:06:12,059 - INFO - Epoch 20 - Validation Accuracy: 40.38%
328
+ 2025-04-03 02:06:13,354 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
329
+ 2025-04-03 02:06:36,136 - INFO - Final validation accuracy: 40.38%
330
+ 2025-04-03 02:06:36,136 - INFO - Best validation accuracy: 41.74%
331
+ 2025-04-03 02:06:37,448 - INFO - Checkpoint saved to outputs/exp2/resnet/fewshot_75_percent/checkpoints/checkpoint_last
332
+ 2025-04-03 02:06:37,448 - INFO - Training completed
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1
+ 2025-04-03 03:18:35,196 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=None, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='vit', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=5), gpu=5, seed=42, num_workers=4, output_dir='./outputs/exp2/vit/fewshot_100_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
2
+ 2025-04-03 03:18:35,196 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
3
+ 2025-04-03 03:19:05,393 - INFO - Initializing RobustMedClip model
4
+ 2025-04-03 03:19:12,439 - INFO - Starting training
5
+ 2025-04-03 03:19:12,439 - INFO - Epoch 1/20
6
+ 2025-04-03 03:19:15,100 - INFO - Train Epoch: 1 [0/139 (0%)] Loss: 5.8767
7
+ 2025-04-03 03:19:31,948 - INFO - Train Epoch: 1 [10/139 (7%)] Loss: 4.6353
8
+ 2025-04-03 03:19:48,918 - INFO - Train Epoch: 1 [20/139 (14%)] Loss: 3.8978
9
+ 2025-04-03 03:20:05,973 - INFO - Train Epoch: 1 [30/139 (22%)] Loss: 2.8041
10
+ 2025-04-03 03:20:23,074 - INFO - Train Epoch: 1 [40/139 (29%)] Loss: 2.4903
11
+ 2025-04-03 03:20:40,223 - INFO - Train Epoch: 1 [50/139 (36%)] Loss: 2.1117
12
+ 2025-04-03 03:20:57,422 - INFO - Train Epoch: 1 [60/139 (43%)] Loss: 1.8705
13
+ 2025-04-03 03:21:14,621 - INFO - Train Epoch: 1 [70/139 (50%)] Loss: 1.9268
14
+ 2025-04-03 03:21:31,851 - INFO - Train Epoch: 1 [80/139 (58%)] Loss: 1.6126
15
+ 2025-04-03 03:21:49,083 - INFO - Train Epoch: 1 [90/139 (65%)] Loss: 1.6640
16
+ 2025-04-03 03:22:06,334 - INFO - Train Epoch: 1 [100/139 (72%)] Loss: 1.6246
17
+ 2025-04-03 03:22:23,581 - INFO - Train Epoch: 1 [110/139 (79%)] Loss: 1.4682
18
+ 2025-04-03 03:22:40,825 - INFO - Train Epoch: 1 [120/139 (86%)] Loss: 1.3303
19
+ 2025-04-03 03:22:58,073 - INFO - Train Epoch: 1 [130/139 (94%)] Loss: 1.2319
20
+ 2025-04-03 03:23:10,502 - INFO - Epoch 1 - Train Loss: 2.2803
21
+ 2025-04-03 03:23:56,079 - INFO - Epoch 1 - Validation Accuracy: 31.71%
22
+ 2025-04-03 03:23:56,080 - INFO - New best validation accuracy: 31.71%
23
+ 2025-04-03 03:23:58,035 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
24
+ 2025-04-03 03:23:59,570 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
25
+ 2025-04-03 03:23:59,570 - INFO - Epoch 2/20
26
+ 2025-04-03 03:24:02,301 - INFO - Train Epoch: 2 [0/139 (0%)] Loss: 1.0871
27
+ 2025-04-03 03:24:19,479 - INFO - Train Epoch: 2 [10/139 (7%)] Loss: 1.0284
28
+ 2025-04-03 03:24:36,713 - INFO - Train Epoch: 2 [20/139 (14%)] Loss: 1.0864
29
+ 2025-04-03 03:24:53,951 - INFO - Train Epoch: 2 [30/139 (22%)] Loss: 1.0471
30
+ 2025-04-03 03:25:11,223 - INFO - Train Epoch: 2 [40/139 (29%)] Loss: 0.8571
31
+ 2025-04-03 03:25:28,503 - INFO - Train Epoch: 2 [50/139 (36%)] Loss: 0.9723
32
+ 2025-04-03 03:25:45,776 - INFO - Train Epoch: 2 [60/139 (43%)] Loss: 0.9472
33
+ 2025-04-03 03:26:03,061 - INFO - Train Epoch: 2 [70/139 (50%)] Loss: 0.8836
34
+ 2025-04-03 03:26:20,339 - INFO - Train Epoch: 2 [80/139 (58%)] Loss: 0.8380
35
+ 2025-04-03 03:26:37,638 - INFO - Train Epoch: 2 [90/139 (65%)] Loss: 0.8418
36
+ 2025-04-03 03:26:54,917 - INFO - Train Epoch: 2 [100/139 (72%)] Loss: 0.7141
37
+ 2025-04-03 03:27:12,215 - INFO - Train Epoch: 2 [110/139 (79%)] Loss: 0.7262
38
+ 2025-04-03 03:27:29,495 - INFO - Train Epoch: 2 [120/139 (86%)] Loss: 0.6968
39
+ 2025-04-03 03:27:46,793 - INFO - Train Epoch: 2 [130/139 (94%)] Loss: 0.7215
40
+ 2025-04-03 03:27:59,276 - INFO - Epoch 2 - Train Loss: 0.9011
41
+ 2025-04-03 03:28:44,756 - INFO - Epoch 2 - Validation Accuracy: 47.94%
42
+ 2025-04-03 03:28:44,757 - INFO - New best validation accuracy: 47.94%
43
+ 2025-04-03 03:28:47,271 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
44
+ 2025-04-03 03:28:49,009 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
45
+ 2025-04-03 03:28:49,009 - INFO - Epoch 3/20
46
+ 2025-04-03 03:28:51,591 - INFO - Train Epoch: 3 [0/139 (0%)] Loss: 0.6718
47
+ 2025-04-03 03:29:08,733 - INFO - Train Epoch: 3 [10/139 (7%)] Loss: 0.5652
48
+ 2025-04-03 03:29:25,916 - INFO - Train Epoch: 3 [20/139 (14%)] Loss: 0.5986
49
+ 2025-04-03 03:29:43,118 - INFO - Train Epoch: 3 [30/139 (22%)] Loss: 0.5277
50
+ 2025-04-03 03:30:00,326 - INFO - Train Epoch: 3 [40/139 (29%)] Loss: 0.5429
51
+ 2025-04-03 03:30:17,538 - INFO - Train Epoch: 3 [50/139 (36%)] Loss: 0.6189
52
+ 2025-04-03 03:30:34,760 - INFO - Train Epoch: 3 [60/139 (43%)] Loss: 0.5689
53
+ 2025-04-03 03:30:51,982 - INFO - Train Epoch: 3 [70/139 (50%)] Loss: 0.5455
54
+ 2025-04-03 03:31:09,210 - INFO - Train Epoch: 3 [80/139 (58%)] Loss: 0.3994
55
+ 2025-04-03 03:31:26,449 - INFO - Train Epoch: 3 [90/139 (65%)] Loss: 0.5070
56
+ 2025-04-03 03:31:43,690 - INFO - Train Epoch: 3 [100/139 (72%)] Loss: 0.4325
57
+ 2025-04-03 03:32:00,939 - INFO - Train Epoch: 3 [110/139 (79%)] Loss: 0.4641
58
+ 2025-04-03 03:32:18,179 - INFO - Train Epoch: 3 [120/139 (86%)] Loss: 0.6050
59
+ 2025-04-03 03:32:35,439 - INFO - Train Epoch: 3 [130/139 (94%)] Loss: 0.5805
60
+ 2025-04-03 03:32:47,872 - INFO - Epoch 3 - Train Loss: 0.5706
61
+ 2025-04-03 03:33:33,242 - INFO - Epoch 3 - Validation Accuracy: 56.56%
62
+ 2025-04-03 03:33:33,242 - INFO - New best validation accuracy: 56.56%
63
+ 2025-04-03 03:33:35,770 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
64
+ 2025-04-03 03:33:37,410 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
65
+ 2025-04-03 03:33:37,410 - INFO - Epoch 4/20
66
+ 2025-04-03 03:33:40,033 - INFO - Train Epoch: 4 [0/139 (0%)] Loss: 0.5116
67
+ 2025-04-03 03:33:57,165 - INFO - Train Epoch: 4 [10/139 (7%)] Loss: 0.4393
68
+ 2025-04-03 03:34:14,336 - INFO - Train Epoch: 4 [20/139 (14%)] Loss: 0.3933
69
+ 2025-04-03 03:34:31,547 - INFO - Train Epoch: 4 [30/139 (22%)] Loss: 0.4841
70
+ 2025-04-03 03:34:48,753 - INFO - Train Epoch: 4 [40/139 (29%)] Loss: 0.5240
71
+ 2025-04-03 03:35:05,968 - INFO - Train Epoch: 4 [50/139 (36%)] Loss: 0.5007
72
+ 2025-04-03 03:35:23,187 - INFO - Train Epoch: 4 [60/139 (43%)] Loss: 0.4502
73
+ 2025-04-03 03:35:40,410 - INFO - Train Epoch: 4 [70/139 (50%)] Loss: 0.4657
74
+ 2025-04-03 03:35:57,640 - INFO - Train Epoch: 4 [80/139 (58%)] Loss: 0.3866
75
+ 2025-04-03 03:36:14,867 - INFO - Train Epoch: 4 [90/139 (65%)] Loss: 0.3480
76
+ 2025-04-03 03:36:32,097 - INFO - Train Epoch: 4 [100/139 (72%)] Loss: 0.3828
77
+ 2025-04-03 03:36:49,328 - INFO - Train Epoch: 4 [110/139 (79%)] Loss: 0.5205
78
+ 2025-04-03 03:37:06,561 - INFO - Train Epoch: 4 [120/139 (86%)] Loss: 0.4415
79
+ 2025-04-03 03:37:23,797 - INFO - Train Epoch: 4 [130/139 (94%)] Loss: 0.4608
80
+ 2025-04-03 03:37:36,217 - INFO - Epoch 4 - Train Loss: 0.4333
81
+ 2025-04-03 03:38:21,596 - INFO - Epoch 4 - Validation Accuracy: 60.47%
82
+ 2025-04-03 03:38:21,597 - INFO - New best validation accuracy: 60.47%
83
+ 2025-04-03 03:38:23,987 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
84
+ 2025-04-03 03:38:25,665 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
85
+ 2025-04-03 03:38:25,666 - INFO - Epoch 5/20
86
+ 2025-04-03 03:38:28,321 - INFO - Train Epoch: 5 [0/139 (0%)] Loss: 0.4587
87
+ 2025-04-03 03:38:45,479 - INFO - Train Epoch: 5 [10/139 (7%)] Loss: 0.4666
88
+ 2025-04-03 03:39:02,676 - INFO - Train Epoch: 5 [20/139 (14%)] Loss: 0.3131
89
+ 2025-04-03 03:39:19,892 - INFO - Train Epoch: 5 [30/139 (22%)] Loss: 0.3434
90
+ 2025-04-03 03:39:37,104 - INFO - Train Epoch: 5 [40/139 (29%)] Loss: 0.4523
91
+ 2025-04-03 03:39:54,328 - INFO - Train Epoch: 5 [50/139 (36%)] Loss: 0.2825
92
+ 2025-04-03 03:40:11,559 - INFO - Train Epoch: 5 [60/139 (43%)] Loss: 0.3569
93
+ 2025-04-03 03:40:28,798 - INFO - Train Epoch: 5 [70/139 (50%)] Loss: 0.2694
94
+ 2025-04-03 03:40:46,033 - INFO - Train Epoch: 5 [80/139 (58%)] Loss: 0.3140
95
+ 2025-04-03 03:41:03,268 - INFO - Train Epoch: 5 [90/139 (65%)] Loss: 0.4332
96
+ 2025-04-03 03:41:20,510 - INFO - Train Epoch: 5 [100/139 (72%)] Loss: 0.3074
97
+ 2025-04-03 03:41:37,755 - INFO - Train Epoch: 5 [110/139 (79%)] Loss: 0.2648
98
+ 2025-04-03 03:41:55,007 - INFO - Train Epoch: 5 [120/139 (86%)] Loss: 0.3686
99
+ 2025-04-03 03:42:12,252 - INFO - Train Epoch: 5 [130/139 (94%)] Loss: 0.3267
100
+ 2025-04-03 03:42:24,686 - INFO - Epoch 5 - Train Loss: 0.3611
101
+ 2025-04-03 03:43:10,055 - INFO - Epoch 5 - Validation Accuracy: 61.64%
102
+ 2025-04-03 03:43:10,056 - INFO - New best validation accuracy: 61.64%
103
+ 2025-04-03 03:43:12,588 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
104
+ 2025-04-03 03:43:14,288 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
105
+ 2025-04-03 03:43:14,288 - INFO - Epoch 6/20
106
+ 2025-04-03 03:43:16,883 - INFO - Train Epoch: 6 [0/139 (0%)] Loss: 0.3228
107
+ 2025-04-03 03:43:34,051 - INFO - Train Epoch: 6 [10/139 (7%)] Loss: 0.4016
108
+ 2025-04-03 03:43:51,242 - INFO - Train Epoch: 6 [20/139 (14%)] Loss: 0.3145
109
+ 2025-04-03 03:44:08,458 - INFO - Train Epoch: 6 [30/139 (22%)] Loss: 0.3793
110
+ 2025-04-03 03:44:25,669 - INFO - Train Epoch: 6 [40/139 (29%)] Loss: 0.3910
111
+ 2025-04-03 03:44:42,881 - INFO - Train Epoch: 6 [50/139 (36%)] Loss: 0.3195
112
+ 2025-04-03 03:45:00,095 - INFO - Train Epoch: 6 [60/139 (43%)] Loss: 0.3582
113
+ 2025-04-03 03:45:17,323 - INFO - Train Epoch: 6 [70/139 (50%)] Loss: 0.2837
114
+ 2025-04-03 03:45:34,547 - INFO - Train Epoch: 6 [80/139 (58%)] Loss: 0.3046
115
+ 2025-04-03 03:45:51,777 - INFO - Train Epoch: 6 [90/139 (65%)] Loss: 0.2599
116
+ 2025-04-03 03:46:08,996 - INFO - Train Epoch: 6 [100/139 (72%)] Loss: 0.2806
117
+ 2025-04-03 03:46:26,223 - INFO - Train Epoch: 6 [110/139 (79%)] Loss: 0.3017
118
+ 2025-04-03 03:46:43,455 - INFO - Train Epoch: 6 [120/139 (86%)] Loss: 0.2804
119
+ 2025-04-03 03:47:00,708 - INFO - Train Epoch: 6 [130/139 (94%)] Loss: 0.3804
120
+ 2025-04-03 03:47:13,153 - INFO - Epoch 6 - Train Loss: 0.3139
121
+ 2025-04-03 03:47:58,619 - INFO - Epoch 6 - Validation Accuracy: 65.85%
122
+ 2025-04-03 03:47:58,620 - INFO - New best validation accuracy: 65.85%
123
+ 2025-04-03 03:48:01,119 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
124
+ 2025-04-03 03:48:02,712 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
125
+ 2025-04-03 03:48:02,713 - INFO - Epoch 7/20
126
+ 2025-04-03 03:48:05,316 - INFO - Train Epoch: 7 [0/139 (0%)] Loss: 0.3005
127
+ 2025-04-03 03:48:22,506 - INFO - Train Epoch: 7 [10/139 (7%)] Loss: 0.3116
128
+ 2025-04-03 03:48:39,690 - INFO - Train Epoch: 7 [20/139 (14%)] Loss: 0.2720
129
+ 2025-04-03 03:48:56,916 - INFO - Train Epoch: 7 [30/139 (22%)] Loss: 0.3238
130
+ 2025-04-03 03:49:14,132 - INFO - Train Epoch: 7 [40/139 (29%)] Loss: 0.2959
131
+ 2025-04-03 03:49:31,385 - INFO - Train Epoch: 7 [50/139 (36%)] Loss: 0.2731
132
+ 2025-04-03 03:49:48,637 - INFO - Train Epoch: 7 [60/139 (43%)] Loss: 0.2286
133
+ 2025-04-03 03:50:05,883 - INFO - Train Epoch: 7 [70/139 (50%)] Loss: 0.2315
134
+ 2025-04-03 03:50:23,136 - INFO - Train Epoch: 7 [80/139 (58%)] Loss: 0.2146
135
+ 2025-04-03 03:50:40,394 - INFO - Train Epoch: 7 [90/139 (65%)] Loss: 0.2631
136
+ 2025-04-03 03:50:57,647 - INFO - Train Epoch: 7 [100/139 (72%)] Loss: 0.2277
137
+ 2025-04-03 03:51:14,922 - INFO - Train Epoch: 7 [110/139 (79%)] Loss: 0.2548
138
+ 2025-04-03 03:51:32,183 - INFO - Train Epoch: 7 [120/139 (86%)] Loss: 0.2491
139
+ 2025-04-03 03:51:49,448 - INFO - Train Epoch: 7 [130/139 (94%)] Loss: 0.2799
140
+ 2025-04-03 03:52:01,911 - INFO - Epoch 7 - Train Loss: 0.2851
141
+ 2025-04-03 03:52:47,849 - INFO - Epoch 7 - Validation Accuracy: 67.91%
142
+ 2025-04-03 03:52:47,849 - INFO - New best validation accuracy: 67.91%
143
+ 2025-04-03 03:52:50,330 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
144
+ 2025-04-03 03:52:52,130 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
145
+ 2025-04-03 03:52:52,130 - INFO - Epoch 8/20
146
+ 2025-04-03 03:52:54,697 - INFO - Train Epoch: 8 [0/139 (0%)] Loss: 0.2306
147
+ 2025-04-03 03:53:11,831 - INFO - Train Epoch: 8 [10/139 (7%)] Loss: 0.2622
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+ 2025-04-03 03:53:29,001 - INFO - Train Epoch: 8 [20/139 (14%)] Loss: 0.3614
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+ 2025-04-03 03:53:46,207 - INFO - Train Epoch: 8 [30/139 (22%)] Loss: 0.3005
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+ 2025-04-03 03:54:03,417 - INFO - Train Epoch: 8 [40/139 (29%)] Loss: 0.2647
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+ 2025-04-03 03:54:20,623 - INFO - Train Epoch: 8 [50/139 (36%)] Loss: 0.2225
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+ 2025-04-03 03:54:37,839 - INFO - Train Epoch: 8 [60/139 (43%)] Loss: 0.3808
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+ 2025-04-03 03:54:55,063 - INFO - Train Epoch: 8 [70/139 (50%)] Loss: 0.2648
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+ 2025-04-03 03:55:12,293 - INFO - Train Epoch: 8 [80/139 (58%)] Loss: 0.2883
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+ 2025-04-03 03:55:29,536 - INFO - Train Epoch: 8 [90/139 (65%)] Loss: 0.2767
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+ 2025-04-03 03:55:46,765 - INFO - Train Epoch: 8 [100/139 (72%)] Loss: 0.2244
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+ 2025-04-03 03:56:04,004 - INFO - Train Epoch: 8 [110/139 (79%)] Loss: 0.2068
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+ 2025-04-03 03:56:21,246 - INFO - Train Epoch: 8 [120/139 (86%)] Loss: 0.2332
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+ 2025-04-03 03:56:38,498 - INFO - Train Epoch: 8 [130/139 (94%)] Loss: 0.2448
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+ 2025-04-03 03:56:50,928 - INFO - Epoch 8 - Train Loss: 0.2601
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+ 2025-04-03 03:57:36,227 - INFO - Epoch 8 - Validation Accuracy: 71.27%
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+ 2025-04-03 03:57:36,227 - INFO - New best validation accuracy: 71.27%
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+ 2025-04-03 03:57:38,740 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-03 03:57:40,344 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 03:57:40,344 - INFO - Epoch 9/20
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+ 2025-04-03 03:57:42,880 - INFO - Train Epoch: 9 [0/139 (0%)] Loss: 0.2574
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+ 2025-04-03 03:58:00,010 - INFO - Train Epoch: 9 [10/139 (7%)] Loss: 0.3646
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+ 2025-04-03 03:58:17,224 - INFO - Train Epoch: 9 [20/139 (14%)] Loss: 0.2166
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+ 2025-04-03 03:58:34,448 - INFO - Train Epoch: 9 [30/139 (22%)] Loss: 0.2814
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+ 2025-04-03 03:58:51,655 - INFO - Train Epoch: 9 [40/139 (29%)] Loss: 0.2854
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+ 2025-04-03 03:59:08,866 - INFO - Train Epoch: 9 [50/139 (36%)] Loss: 0.2795
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+ 2025-04-03 03:59:26,078 - INFO - Train Epoch: 9 [60/139 (43%)] Loss: 0.1893
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+ 2025-04-03 03:59:43,286 - INFO - Train Epoch: 9 [70/139 (50%)] Loss: 0.2925
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+ 2025-04-03 04:00:00,509 - INFO - Train Epoch: 9 [80/139 (58%)] Loss: 0.2155
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+ 2025-04-03 04:00:17,729 - INFO - Train Epoch: 9 [90/139 (65%)] Loss: 0.1658
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+ 2025-04-03 04:00:34,964 - INFO - Train Epoch: 9 [100/139 (72%)] Loss: 0.2003
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+ 2025-04-03 04:00:52,194 - INFO - Train Epoch: 9 [110/139 (79%)] Loss: 0.2587
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+ 2025-04-03 04:01:09,433 - INFO - Train Epoch: 9 [120/139 (86%)] Loss: 0.2920
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+ 2025-04-03 04:01:26,697 - INFO - Train Epoch: 9 [130/139 (94%)] Loss: 0.2020
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+ 2025-04-03 04:01:39,128 - INFO - Epoch 9 - Train Loss: 0.2452
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+ 2025-04-03 04:02:24,637 - INFO - Epoch 9 - Validation Accuracy: 69.91%
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+ 2025-04-03 04:02:26,315 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 04:02:26,315 - INFO - Epoch 10/20
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+ 2025-04-03 04:02:28,862 - INFO - Train Epoch: 10 [0/139 (0%)] Loss: 0.2645
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+ 2025-04-03 04:02:46,187 - INFO - Train Epoch: 10 [10/139 (7%)] Loss: 0.2562
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+ 2025-04-03 04:03:03,539 - INFO - Train Epoch: 10 [20/139 (14%)] Loss: 0.3532
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+ 2025-04-03 04:03:20,820 - INFO - Train Epoch: 10 [30/139 (22%)] Loss: 0.2309
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+ 2025-04-03 04:03:38,175 - INFO - Train Epoch: 10 [40/139 (29%)] Loss: 0.1824
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+ 2025-04-03 04:03:55,455 - INFO - Train Epoch: 10 [50/139 (36%)] Loss: 0.1999
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+ 2025-04-03 04:04:12,789 - INFO - Train Epoch: 10 [60/139 (43%)] Loss: 0.2108
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+ 2025-04-03 04:04:30,128 - INFO - Train Epoch: 10 [70/139 (50%)] Loss: 0.2168
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+ 2025-04-03 04:04:47,419 - INFO - Train Epoch: 10 [80/139 (58%)] Loss: 0.2012
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+ 2025-04-03 04:05:04,701 - INFO - Train Epoch: 10 [90/139 (65%)] Loss: 0.1827
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+ 2025-04-03 04:05:21,981 - INFO - Train Epoch: 10 [100/139 (72%)] Loss: 0.2751
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+ 2025-04-03 04:05:39,288 - INFO - Train Epoch: 10 [110/139 (79%)] Loss: 0.1334
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+ 2025-04-03 04:05:56,573 - INFO - Train Epoch: 10 [120/139 (86%)] Loss: 0.2431
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+ 2025-04-03 04:06:13,905 - INFO - Train Epoch: 10 [130/139 (94%)] Loss: 0.2546
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+ 2025-04-03 04:06:26,387 - INFO - Epoch 10 - Train Loss: 0.2217
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+ 2025-04-03 04:07:11,939 - INFO - Epoch 10 - Validation Accuracy: 71.34%
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+ 2025-04-03 04:07:11,939 - INFO - New best validation accuracy: 71.34%
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+ 2025-04-03 04:07:14,354 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-03 04:07:16,045 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 04:07:16,045 - INFO - Epoch 11/20
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+ 2025-04-03 04:07:18,620 - INFO - Train Epoch: 11 [0/139 (0%)] Loss: 0.1562
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+ 2025-04-03 04:07:35,781 - INFO - Train Epoch: 11 [10/139 (7%)] Loss: 0.1967
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+ 2025-04-03 04:07:52,964 - INFO - Train Epoch: 11 [20/139 (14%)] Loss: 0.2085
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+ 2025-04-03 04:08:10,160 - INFO - Train Epoch: 11 [30/139 (22%)] Loss: 0.2122
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+ 2025-04-03 04:08:27,377 - INFO - Train Epoch: 11 [40/139 (29%)] Loss: 0.1646
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+ 2025-04-03 04:08:44,593 - INFO - Train Epoch: 11 [50/139 (36%)] Loss: 0.2085
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+ 2025-04-03 04:09:01,824 - INFO - Train Epoch: 11 [60/139 (43%)] Loss: 0.1830
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+ 2025-04-03 04:09:19,051 - INFO - Train Epoch: 11 [70/139 (50%)] Loss: 0.2032
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+ 2025-04-03 04:09:36,285 - INFO - Train Epoch: 11 [80/139 (58%)] Loss: 0.1530
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+ 2025-04-03 04:09:53,515 - INFO - Train Epoch: 11 [90/139 (65%)] Loss: 0.2160
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+ 2025-04-03 04:10:10,754 - INFO - Train Epoch: 11 [100/139 (72%)] Loss: 0.2253
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+ 2025-04-03 04:10:27,990 - INFO - Train Epoch: 11 [110/139 (79%)] Loss: 0.1919
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+ 2025-04-03 04:10:45,222 - INFO - Train Epoch: 11 [120/139 (86%)] Loss: 0.2389
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+ 2025-04-03 04:11:02,463 - INFO - Train Epoch: 11 [130/139 (94%)] Loss: 0.2435
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+ 2025-04-03 04:11:14,886 - INFO - Epoch 11 - Train Loss: 0.2111
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+ 2025-04-03 04:12:00,490 - INFO - Epoch 11 - Validation Accuracy: 73.77%
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+ 2025-04-03 04:12:00,490 - INFO - New best validation accuracy: 73.77%
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+ 2025-04-03 04:12:03,047 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-03 04:12:04,739 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 04:12:04,739 - INFO - Epoch 12/20
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+ 2025-04-03 04:12:07,380 - INFO - Train Epoch: 12 [0/139 (0%)] Loss: 0.1640
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+ 2025-04-03 04:12:24,524 - INFO - Train Epoch: 12 [10/139 (7%)] Loss: 0.1995
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+ 2025-04-03 04:12:41,704 - INFO - Train Epoch: 12 [20/139 (14%)] Loss: 0.2430
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+ 2025-04-03 04:12:58,908 - INFO - Train Epoch: 12 [30/139 (22%)] Loss: 0.2049
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+ 2025-04-03 04:13:16,105 - INFO - Train Epoch: 12 [40/139 (29%)] Loss: 0.1481
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+ 2025-04-03 04:13:33,341 - INFO - Train Epoch: 12 [50/139 (36%)] Loss: 0.2741
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+ 2025-04-03 04:13:50,562 - INFO - Train Epoch: 12 [60/139 (43%)] Loss: 0.1659
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+ 2025-04-03 04:14:07,784 - INFO - Train Epoch: 12 [70/139 (50%)] Loss: 0.2764
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+ 2025-04-03 04:14:25,012 - INFO - Train Epoch: 12 [80/139 (58%)] Loss: 0.1423
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+ 2025-04-03 04:14:42,243 - INFO - Train Epoch: 12 [90/139 (65%)] Loss: 0.3136
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+ 2025-04-03 04:14:59,465 - INFO - Train Epoch: 12 [100/139 (72%)] Loss: 0.1624
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+ 2025-04-03 04:15:16,694 - INFO - Train Epoch: 12 [110/139 (79%)] Loss: 0.2006
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+ 2025-04-03 04:15:33,912 - INFO - Train Epoch: 12 [120/139 (86%)] Loss: 0.1815
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+ 2025-04-03 04:15:51,146 - INFO - Train Epoch: 12 [130/139 (94%)] Loss: 0.1495
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+ 2025-04-03 04:16:03,558 - INFO - Epoch 12 - Train Loss: 0.2040
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+ 2025-04-03 04:16:48,591 - INFO - Epoch 12 - Validation Accuracy: 75.49%
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+ 2025-04-03 04:16:48,591 - INFO - New best validation accuracy: 75.49%
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+ 2025-04-03 04:16:50,987 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-03 04:16:52,721 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 04:16:52,721 - INFO - Epoch 13/20
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+ 2025-04-03 04:16:55,311 - INFO - Train Epoch: 13 [0/139 (0%)] Loss: 0.1150
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+ 2025-04-03 04:17:12,460 - INFO - Train Epoch: 13 [10/139 (7%)] Loss: 0.2928
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+ 2025-04-03 04:17:29,642 - INFO - Train Epoch: 13 [20/139 (14%)] Loss: 0.2189
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+ 2025-04-03 04:17:46,846 - INFO - Train Epoch: 13 [30/139 (22%)] Loss: 0.1628
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+ 2025-04-03 04:18:04,068 - INFO - Train Epoch: 13 [40/139 (29%)] Loss: 0.1887
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+ 2025-04-03 04:18:21,309 - INFO - Train Epoch: 13 [50/139 (36%)] Loss: 0.1507
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+ 2025-04-03 04:18:38,566 - INFO - Train Epoch: 13 [60/139 (43%)] Loss: 0.2340
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+ 2025-04-03 04:18:55,808 - INFO - Train Epoch: 13 [70/139 (50%)] Loss: 0.2325
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+ 2025-04-03 04:19:13,071 - INFO - Train Epoch: 13 [80/139 (58%)] Loss: 0.2012
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+ 2025-04-03 04:19:30,347 - INFO - Train Epoch: 13 [90/139 (65%)] Loss: 0.2033
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+ 2025-04-03 04:19:47,612 - INFO - Train Epoch: 13 [100/139 (72%)] Loss: 0.2368
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+ 2025-04-03 04:20:04,871 - INFO - Train Epoch: 13 [110/139 (79%)] Loss: 0.2086
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+ 2025-04-03 04:20:22,134 - INFO - Train Epoch: 13 [120/139 (86%)] Loss: 0.2314
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+ 2025-04-03 04:20:39,393 - INFO - Train Epoch: 13 [130/139 (94%)] Loss: 0.1996
258
+ 2025-04-03 04:20:51,839 - INFO - Epoch 13 - Train Loss: 0.1937
259
+ 2025-04-03 04:21:37,289 - INFO - Epoch 13 - Validation Accuracy: 76.45%
260
+ 2025-04-03 04:21:37,289 - INFO - New best validation accuracy: 76.45%
261
+ 2025-04-03 04:21:39,724 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
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+ 2025-04-03 04:21:41,406 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 04:21:41,406 - INFO - Epoch 14/20
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+ 2025-04-03 04:21:43,995 - INFO - Train Epoch: 14 [0/139 (0%)] Loss: 0.1591
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+ 2025-04-03 04:22:01,159 - INFO - Train Epoch: 14 [10/139 (7%)] Loss: 0.1571
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+ 2025-04-03 04:22:18,359 - INFO - Train Epoch: 14 [20/139 (14%)] Loss: 0.2044
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+ 2025-04-03 04:22:35,555 - INFO - Train Epoch: 14 [30/139 (22%)] Loss: 0.2031
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+ 2025-04-03 04:22:52,778 - INFO - Train Epoch: 14 [40/139 (29%)] Loss: 0.2091
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+ 2025-04-03 04:23:10,005 - INFO - Train Epoch: 14 [50/139 (36%)] Loss: 0.2630
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+ 2025-04-03 04:23:27,240 - INFO - Train Epoch: 14 [60/139 (43%)] Loss: 0.1851
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+ 2025-04-03 04:23:44,476 - INFO - Train Epoch: 14 [70/139 (50%)] Loss: 0.2324
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+ 2025-04-03 04:24:01,718 - INFO - Train Epoch: 14 [80/139 (58%)] Loss: 0.1412
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+ 2025-04-03 04:24:18,960 - INFO - Train Epoch: 14 [90/139 (65%)] Loss: 0.1480
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+ 2025-04-03 04:24:36,206 - INFO - Train Epoch: 14 [100/139 (72%)] Loss: 0.1652
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+ 2025-04-03 04:24:53,446 - INFO - Train Epoch: 14 [110/139 (79%)] Loss: 0.1906
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+ 2025-04-03 04:25:10,686 - INFO - Train Epoch: 14 [120/139 (86%)] Loss: 0.1689
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+ 2025-04-03 04:25:27,933 - INFO - Train Epoch: 14 [130/139 (94%)] Loss: 0.1773
278
+ 2025-04-03 04:25:40,389 - INFO - Epoch 14 - Train Loss: 0.1922
279
+ 2025-04-03 04:26:25,817 - INFO - Epoch 14 - Validation Accuracy: 74.30%
280
+ 2025-04-03 04:26:27,645 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
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+ 2025-04-03 04:26:27,645 - INFO - Epoch 15/20
282
+ 2025-04-03 04:26:30,224 - INFO - Train Epoch: 15 [0/139 (0%)] Loss: 0.1892
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+ 2025-04-03 04:26:47,423 - INFO - Train Epoch: 15 [10/139 (7%)] Loss: 0.1855
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+ 2025-04-03 04:27:04,646 - INFO - Train Epoch: 15 [20/139 (14%)] Loss: 0.1359
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+ 2025-04-03 04:27:21,866 - INFO - Train Epoch: 15 [30/139 (22%)] Loss: 0.1730
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+ 2025-04-03 04:27:39,114 - INFO - Train Epoch: 15 [40/139 (29%)] Loss: 0.1889
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+ 2025-04-03 04:27:56,357 - INFO - Train Epoch: 15 [50/139 (36%)] Loss: 0.2072
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+ 2025-04-03 04:28:13,593 - INFO - Train Epoch: 15 [60/139 (43%)] Loss: 0.1688
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+ 2025-04-03 04:28:30,836 - INFO - Train Epoch: 15 [70/139 (50%)] Loss: 0.1811
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+ 2025-04-03 04:28:48,077 - INFO - Train Epoch: 15 [80/139 (58%)] Loss: 0.1827
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+ 2025-04-03 04:29:05,316 - INFO - Train Epoch: 15 [90/139 (65%)] Loss: 0.1381
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+ 2025-04-03 04:29:22,556 - INFO - Train Epoch: 15 [100/139 (72%)] Loss: 0.2284
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+ 2025-04-03 04:29:39,793 - INFO - Train Epoch: 15 [110/139 (79%)] Loss: 0.1678
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+ 2025-04-03 04:29:57,037 - INFO - Train Epoch: 15 [120/139 (86%)] Loss: 0.2203
295
+ 2025-04-03 04:30:14,282 - INFO - Train Epoch: 15 [130/139 (94%)] Loss: 0.2074
296
+ 2025-04-03 04:30:26,712 - INFO - Epoch 15 - Train Loss: 0.1738
297
+ 2025-04-03 04:31:12,098 - INFO - Epoch 15 - Validation Accuracy: 77.01%
298
+ 2025-04-03 04:31:12,098 - INFO - New best validation accuracy: 77.01%
299
+ 2025-04-03 04:31:14,596 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
300
+ 2025-04-03 04:31:16,296 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
301
+ 2025-04-03 04:31:16,296 - INFO - Epoch 16/20
302
+ 2025-04-03 04:31:18,880 - INFO - Train Epoch: 16 [0/139 (0%)] Loss: 0.2957
303
+ 2025-04-03 04:31:36,021 - INFO - Train Epoch: 16 [10/139 (7%)] Loss: 0.2599
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+ 2025-04-03 04:31:53,214 - INFO - Train Epoch: 16 [20/139 (14%)] Loss: 0.1461
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+ 2025-04-03 04:32:10,421 - INFO - Train Epoch: 16 [30/139 (22%)] Loss: 0.1998
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+ 2025-04-03 04:32:27,642 - INFO - Train Epoch: 16 [40/139 (29%)] Loss: 0.1349
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+ 2025-04-03 04:32:44,865 - INFO - Train Epoch: 16 [50/139 (36%)] Loss: 0.1924
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+ 2025-04-03 04:33:02,089 - INFO - Train Epoch: 16 [60/139 (43%)] Loss: 0.1677
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+ 2025-04-03 04:33:19,358 - INFO - Train Epoch: 16 [70/139 (50%)] Loss: 0.1726
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+ 2025-04-03 04:33:36,598 - INFO - Train Epoch: 16 [80/139 (58%)] Loss: 0.1536
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+ 2025-04-03 04:33:53,845 - INFO - Train Epoch: 16 [90/139 (65%)] Loss: 0.1532
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+ 2025-04-03 04:34:11,091 - INFO - Train Epoch: 16 [100/139 (72%)] Loss: 0.1670
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+ 2025-04-03 04:34:28,344 - INFO - Train Epoch: 16 [110/139 (79%)] Loss: 0.1153
314
+ 2025-04-03 04:34:45,589 - INFO - Train Epoch: 16 [120/139 (86%)] Loss: 0.1296
315
+ 2025-04-03 04:35:02,835 - INFO - Train Epoch: 16 [130/139 (94%)] Loss: 0.1737
316
+ 2025-04-03 04:35:15,274 - INFO - Epoch 16 - Train Loss: 0.1713
317
+ 2025-04-03 04:36:00,726 - INFO - Epoch 16 - Validation Accuracy: 76.59%
318
+ 2025-04-03 04:36:02,533 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
319
+ 2025-04-03 04:36:02,533 - INFO - Epoch 17/20
320
+ 2025-04-03 04:36:05,137 - INFO - Train Epoch: 17 [0/139 (0%)] Loss: 0.1455
321
+ 2025-04-03 04:36:22,334 - INFO - Train Epoch: 17 [10/139 (7%)] Loss: 0.1546
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+ 2025-04-03 04:36:39,534 - INFO - Train Epoch: 17 [20/139 (14%)] Loss: 0.1998
323
+ 2025-04-03 04:36:56,733 - INFO - Train Epoch: 17 [30/139 (22%)] Loss: 0.1276
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+ 2025-04-03 04:37:13,950 - INFO - Train Epoch: 17 [40/139 (29%)] Loss: 0.1946
325
+ 2025-04-03 04:37:31,177 - INFO - Train Epoch: 17 [50/139 (36%)] Loss: 0.1809
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+ 2025-04-03 04:37:48,398 - INFO - Train Epoch: 17 [60/139 (43%)] Loss: 0.2023
327
+ 2025-04-03 04:38:05,622 - INFO - Train Epoch: 17 [70/139 (50%)] Loss: 0.1823
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+ 2025-04-03 04:38:22,844 - INFO - Train Epoch: 17 [80/139 (58%)] Loss: 0.1065
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+ 2025-04-03 04:38:40,072 - INFO - Train Epoch: 17 [90/139 (65%)] Loss: 0.2459
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+ 2025-04-03 04:38:57,302 - INFO - Train Epoch: 17 [100/139 (72%)] Loss: 0.1396
331
+ 2025-04-03 04:39:14,570 - INFO - Train Epoch: 17 [110/139 (79%)] Loss: 0.1369
332
+ 2025-04-03 04:39:31,818 - INFO - Train Epoch: 17 [120/139 (86%)] Loss: 0.1452
333
+ 2025-04-03 04:39:49,057 - INFO - Train Epoch: 17 [130/139 (94%)] Loss: 0.1603
334
+ 2025-04-03 04:40:01,483 - INFO - Epoch 17 - Train Loss: 0.1608
335
+ 2025-04-03 04:40:46,728 - INFO - Epoch 17 - Validation Accuracy: 77.42%
336
+ 2025-04-03 04:40:46,728 - INFO - New best validation accuracy: 77.42%
337
+ 2025-04-03 04:40:49,289 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
338
+ 2025-04-03 04:40:51,021 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
339
+ 2025-04-03 04:40:51,021 - INFO - Epoch 18/20
340
+ 2025-04-03 04:40:53,596 - INFO - Train Epoch: 18 [0/139 (0%)] Loss: 0.1768
341
+ 2025-04-03 04:41:10,744 - INFO - Train Epoch: 18 [10/139 (7%)] Loss: 0.1613
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+ 2025-04-03 04:41:27,941 - INFO - Train Epoch: 18 [20/139 (14%)] Loss: 0.1985
343
+ 2025-04-03 04:41:45,138 - INFO - Train Epoch: 18 [30/139 (22%)] Loss: 0.1849
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+ 2025-04-03 04:42:02,347 - INFO - Train Epoch: 18 [40/139 (29%)] Loss: 0.1550
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+ 2025-04-03 04:42:19,567 - INFO - Train Epoch: 18 [50/139 (36%)] Loss: 0.1380
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+ 2025-04-03 04:42:36,790 - INFO - Train Epoch: 18 [60/139 (43%)] Loss: 0.1527
347
+ 2025-04-03 04:42:54,020 - INFO - Train Epoch: 18 [70/139 (50%)] Loss: 0.1936
348
+ 2025-04-03 04:43:11,257 - INFO - Train Epoch: 18 [80/139 (58%)] Loss: 0.1689
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+ 2025-04-03 04:43:28,504 - INFO - Train Epoch: 18 [90/139 (65%)] Loss: 0.1880
350
+ 2025-04-03 04:43:45,740 - INFO - Train Epoch: 18 [100/139 (72%)] Loss: 0.1330
351
+ 2025-04-03 04:44:02,987 - INFO - Train Epoch: 18 [110/139 (79%)] Loss: 0.1374
352
+ 2025-04-03 04:44:20,230 - INFO - Train Epoch: 18 [120/139 (86%)] Loss: 0.1223
353
+ 2025-04-03 04:44:37,487 - INFO - Train Epoch: 18 [130/139 (94%)] Loss: 0.2065
354
+ 2025-04-03 04:44:49,921 - INFO - Epoch 18 - Train Loss: 0.1618
355
+ 2025-04-03 04:45:35,549 - INFO - Epoch 18 - Validation Accuracy: 78.32%
356
+ 2025-04-03 04:45:35,550 - INFO - New best validation accuracy: 78.32%
357
+ 2025-04-03 04:45:37,521 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/best_model
358
+ 2025-04-03 04:45:38,913 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
359
+ 2025-04-03 04:45:38,913 - INFO - Epoch 19/20
360
+ 2025-04-03 04:45:41,846 - INFO - Train Epoch: 19 [0/139 (0%)] Loss: 0.1864
361
+ 2025-04-03 04:45:59,174 - INFO - Train Epoch: 19 [10/139 (7%)] Loss: 0.1124
362
+ 2025-04-03 04:46:16,434 - INFO - Train Epoch: 19 [20/139 (14%)] Loss: 0.1610
363
+ 2025-04-03 04:46:33,676 - INFO - Train Epoch: 19 [30/139 (22%)] Loss: 0.1842
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+ 2025-04-03 04:46:50,923 - INFO - Train Epoch: 19 [40/139 (29%)] Loss: 0.2374
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+ 2025-04-03 04:47:08,191 - INFO - Train Epoch: 19 [50/139 (36%)] Loss: 0.1881
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+ 2025-04-03 04:47:25,455 - INFO - Train Epoch: 19 [60/139 (43%)] Loss: 0.1744
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+ 2025-04-03 04:47:42,727 - INFO - Train Epoch: 19 [70/139 (50%)] Loss: 0.1676
368
+ 2025-04-03 04:47:59,985 - INFO - Train Epoch: 19 [80/139 (58%)] Loss: 0.1444
369
+ 2025-04-03 04:48:17,256 - INFO - Train Epoch: 19 [90/139 (65%)] Loss: 0.1523
370
+ 2025-04-03 04:48:34,510 - INFO - Train Epoch: 19 [100/139 (72%)] Loss: 0.1521
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+ 2025-04-03 04:48:51,783 - INFO - Train Epoch: 19 [110/139 (79%)] Loss: 0.1454
372
+ 2025-04-03 04:49:09,044 - INFO - Train Epoch: 19 [120/139 (86%)] Loss: 0.0915
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+ 2025-04-03 04:49:26,379 - INFO - Train Epoch: 19 [130/139 (94%)] Loss: 0.1832
374
+ 2025-04-03 04:49:39,026 - INFO - Epoch 19 - Train Loss: 0.1607
375
+ 2025-04-03 04:50:24,010 - INFO - Epoch 19 - Validation Accuracy: 76.83%
376
+ 2025-04-03 04:50:25,747 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
377
+ 2025-04-03 04:50:25,747 - INFO - Epoch 20/20
378
+ 2025-04-03 04:50:28,284 - INFO - Train Epoch: 20 [0/139 (0%)] Loss: 0.1563
379
+ 2025-04-03 04:50:45,542 - INFO - Train Epoch: 20 [10/139 (7%)] Loss: 0.1466
380
+ 2025-04-03 04:51:02,730 - INFO - Train Epoch: 20 [20/139 (14%)] Loss: 0.1084
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+ 2025-04-03 04:51:19,992 - INFO - Train Epoch: 20 [30/139 (22%)] Loss: 0.1708
382
+ 2025-04-03 04:51:37,219 - INFO - Train Epoch: 20 [40/139 (29%)] Loss: 0.1509
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+ 2025-04-03 04:51:54,535 - INFO - Train Epoch: 20 [50/139 (36%)] Loss: 0.1640
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+ 2025-04-03 04:52:11,841 - INFO - Train Epoch: 20 [60/139 (43%)] Loss: 0.1972
385
+ 2025-04-03 04:52:29,090 - INFO - Train Epoch: 20 [70/139 (50%)] Loss: 0.1213
386
+ 2025-04-03 04:52:46,454 - INFO - Train Epoch: 20 [80/139 (58%)] Loss: 0.1498
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+ 2025-04-03 04:53:03,700 - INFO - Train Epoch: 20 [90/139 (65%)] Loss: 0.1439
388
+ 2025-04-03 04:53:21,023 - INFO - Train Epoch: 20 [100/139 (72%)] Loss: 0.1072
389
+ 2025-04-03 04:53:38,284 - INFO - Train Epoch: 20 [110/139 (79%)] Loss: 0.1488
390
+ 2025-04-03 04:53:55,580 - INFO - Train Epoch: 20 [120/139 (86%)] Loss: 0.1163
391
+ 2025-04-03 04:54:12,868 - INFO - Train Epoch: 20 [130/139 (94%)] Loss: 0.1888
392
+ 2025-04-03 04:54:25,326 - INFO - Epoch 20 - Train Loss: 0.1505
393
+ 2025-04-03 04:55:10,744 - INFO - Epoch 20 - Validation Accuracy: 76.78%
394
+ 2025-04-03 04:55:12,553 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
395
+ 2025-04-03 04:55:57,657 - INFO - Final validation accuracy: 76.78%
396
+ 2025-04-03 04:55:57,657 - INFO - Best validation accuracy: 78.32%
397
+ 2025-04-03 04:55:59,372 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_100_percent/checkpoints/checkpoint_last
398
+ 2025-04-03 04:55:59,372 - INFO - Training completed
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1
+ 2025-04-03 00:54:22,676 - INFO - Arguments: Namespace(datasets=['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist'], use_corruptions=False, severity_range=[1, 3], corruption_types=None, fewshot=0.1, epochs=20, batch_size=256, lr=0.0001, weight_decay=0.0001, temperature=2.0, alpha=0.5, backbone='vit', lora_rank=16, lora_alpha=16, lora_dropout=0.1, device=device(type='cuda', index=5), gpu=5, seed=42, num_workers=4, output_dir='./outputs/exp2/vit/fewshot_10_percent', log_interval=10, save_interval=1, eval_interval=1, resume=None)
2
+ 2025-04-03 00:54:22,677 - INFO - Creating dataloaders for datasets: ['aml', 'fundus', 'mammo_calc', 'mammo_mass', 'pneumonia', 'oct', 'pbc', 'bloodmnist', 'breastmnist', 'octmnist', 'pneumoniamnist', 'retinamnist']
3
+ 2025-04-03 00:54:51,895 - INFO - Initializing RobustMedClip model
4
+ 2025-04-03 00:55:05,615 - INFO - Starting training
5
+ 2025-04-03 00:55:05,616 - INFO - Epoch 1/20
6
+ 2025-04-03 00:55:08,850 - INFO - Train Epoch: 1 [0/14 (0%)] Loss: 5.0796
7
+ 2025-04-03 00:55:25,611 - INFO - Train Epoch: 1 [10/14 (71%)] Loss: 3.8356
8
+ 2025-04-03 00:55:30,371 - INFO - Epoch 1 - Train Loss: 4.2299
9
+ 2025-04-03 00:56:14,276 - INFO - Epoch 1 - Validation Accuracy: 5.66%
10
+ 2025-04-03 00:56:14,276 - INFO - New best validation accuracy: 5.66%
11
+ 2025-04-03 00:56:15,778 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
12
+ 2025-04-03 00:56:17,312 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
13
+ 2025-04-03 00:56:17,312 - INFO - Epoch 2/20
14
+ 2025-04-03 00:56:19,847 - INFO - Train Epoch: 2 [0/14 (0%)] Loss: 3.1250
15
+ 2025-04-03 00:56:36,792 - INFO - Train Epoch: 2 [10/14 (71%)] Loss: 2.6467
16
+ 2025-04-03 00:56:41,650 - INFO - Epoch 2 - Train Loss: 2.8492
17
+ 2025-04-03 00:57:26,069 - INFO - Epoch 2 - Validation Accuracy: 6.78%
18
+ 2025-04-03 00:57:26,069 - INFO - New best validation accuracy: 6.78%
19
+ 2025-04-03 00:57:27,763 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
20
+ 2025-04-03 00:57:29,531 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
21
+ 2025-04-03 00:57:29,532 - INFO - Epoch 3/20
22
+ 2025-04-03 00:57:32,148 - INFO - Train Epoch: 3 [0/14 (0%)] Loss: 2.6190
23
+ 2025-04-03 00:57:49,227 - INFO - Train Epoch: 3 [10/14 (71%)] Loss: 2.1194
24
+ 2025-04-03 00:57:54,094 - INFO - Epoch 3 - Train Loss: 2.2764
25
+ 2025-04-03 00:58:38,916 - INFO - Epoch 3 - Validation Accuracy: 7.82%
26
+ 2025-04-03 00:58:38,916 - INFO - New best validation accuracy: 7.82%
27
+ 2025-04-03 00:58:40,644 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
28
+ 2025-04-03 00:58:42,327 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
29
+ 2025-04-03 00:58:42,327 - INFO - Epoch 4/20
30
+ 2025-04-03 00:58:44,899 - INFO - Train Epoch: 4 [0/14 (0%)] Loss: 2.0331
31
+ 2025-04-03 00:59:02,002 - INFO - Train Epoch: 4 [10/14 (71%)] Loss: 1.9314
32
+ 2025-04-03 00:59:06,869 - INFO - Epoch 4 - Train Loss: 1.9635
33
+ 2025-04-03 00:59:51,778 - INFO - Epoch 4 - Validation Accuracy: 9.24%
34
+ 2025-04-03 00:59:51,779 - INFO - New best validation accuracy: 9.24%
35
+ 2025-04-03 00:59:53,494 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
36
+ 2025-04-03 00:59:55,213 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
37
+ 2025-04-03 00:59:55,213 - INFO - Epoch 5/20
38
+ 2025-04-03 00:59:57,857 - INFO - Train Epoch: 5 [0/14 (0%)] Loss: 1.8923
39
+ 2025-04-03 01:00:15,068 - INFO - Train Epoch: 5 [10/14 (71%)] Loss: 1.6179
40
+ 2025-04-03 01:00:19,999 - INFO - Epoch 5 - Train Loss: 1.7733
41
+ 2025-04-03 01:01:05,728 - INFO - Epoch 5 - Validation Accuracy: 11.12%
42
+ 2025-04-03 01:01:05,729 - INFO - New best validation accuracy: 11.12%
43
+ 2025-04-03 01:01:07,407 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
44
+ 2025-04-03 01:01:09,183 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
45
+ 2025-04-03 01:01:09,183 - INFO - Epoch 6/20
46
+ 2025-04-03 01:01:11,842 - INFO - Train Epoch: 6 [0/14 (0%)] Loss: 1.8124
47
+ 2025-04-03 01:01:28,896 - INFO - Train Epoch: 6 [10/14 (71%)] Loss: 1.6353
48
+ 2025-04-03 01:01:33,748 - INFO - Epoch 6 - Train Loss: 1.6257
49
+ 2025-04-03 01:02:18,845 - INFO - Epoch 6 - Validation Accuracy: 13.63%
50
+ 2025-04-03 01:02:18,845 - INFO - New best validation accuracy: 13.63%
51
+ 2025-04-03 01:02:20,703 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
52
+ 2025-04-03 01:02:23,417 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
53
+ 2025-04-03 01:02:23,417 - INFO - Epoch 7/20
54
+ 2025-04-03 01:02:26,044 - INFO - Train Epoch: 7 [0/14 (0%)] Loss: 1.6416
55
+ 2025-04-03 01:02:43,248 - INFO - Train Epoch: 7 [10/14 (71%)] Loss: 1.4385
56
+ 2025-04-03 01:02:48,139 - INFO - Epoch 7 - Train Loss: 1.5030
57
+ 2025-04-03 01:03:32,630 - INFO - Epoch 7 - Validation Accuracy: 15.23%
58
+ 2025-04-03 01:03:32,631 - INFO - New best validation accuracy: 15.23%
59
+ 2025-04-03 01:03:34,452 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
60
+ 2025-04-03 01:03:37,035 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
61
+ 2025-04-03 01:03:37,036 - INFO - Epoch 8/20
62
+ 2025-04-03 01:03:39,664 - INFO - Train Epoch: 8 [0/14 (0%)] Loss: 1.4327
63
+ 2025-04-03 01:03:56,901 - INFO - Train Epoch: 8 [10/14 (71%)] Loss: 1.3814
64
+ 2025-04-03 01:04:01,809 - INFO - Epoch 8 - Train Loss: 1.3888
65
+ 2025-04-03 01:04:47,044 - INFO - Epoch 8 - Validation Accuracy: 17.50%
66
+ 2025-04-03 01:04:47,044 - INFO - New best validation accuracy: 17.50%
67
+ 2025-04-03 01:04:48,981 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
68
+ 2025-04-03 01:04:50,759 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
69
+ 2025-04-03 01:04:50,759 - INFO - Epoch 9/20
70
+ 2025-04-03 01:04:53,295 - INFO - Train Epoch: 9 [0/14 (0%)] Loss: 1.2682
71
+ 2025-04-03 01:05:10,493 - INFO - Train Epoch: 9 [10/14 (71%)] Loss: 1.3281
72
+ 2025-04-03 01:05:15,400 - INFO - Epoch 9 - Train Loss: 1.3033
73
+ 2025-04-03 01:06:00,799 - INFO - Epoch 9 - Validation Accuracy: 20.42%
74
+ 2025-04-03 01:06:00,799 - INFO - New best validation accuracy: 20.42%
75
+ 2025-04-03 01:06:03,416 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
76
+ 2025-04-03 01:06:05,212 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
77
+ 2025-04-03 01:06:05,212 - INFO - Epoch 10/20
78
+ 2025-04-03 01:06:07,809 - INFO - Train Epoch: 10 [0/14 (0%)] Loss: 1.2300
79
+ 2025-04-03 01:06:24,878 - INFO - Train Epoch: 10 [10/14 (71%)] Loss: 1.1922
80
+ 2025-04-03 01:06:29,746 - INFO - Epoch 10 - Train Loss: 1.2178
81
+ 2025-04-03 01:07:15,039 - INFO - Epoch 10 - Validation Accuracy: 24.70%
82
+ 2025-04-03 01:07:15,040 - INFO - New best validation accuracy: 24.70%
83
+ 2025-04-03 01:07:16,956 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
84
+ 2025-04-03 01:07:18,634 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
85
+ 2025-04-03 01:07:18,634 - INFO - Epoch 11/20
86
+ 2025-04-03 01:07:21,230 - INFO - Train Epoch: 11 [0/14 (0%)] Loss: 1.0525
87
+ 2025-04-03 01:07:38,378 - INFO - Train Epoch: 11 [10/14 (71%)] Loss: 1.2115
88
+ 2025-04-03 01:07:43,269 - INFO - Epoch 11 - Train Loss: 1.1439
89
+ 2025-04-03 01:08:28,021 - INFO - Epoch 11 - Validation Accuracy: 29.56%
90
+ 2025-04-03 01:08:28,022 - INFO - New best validation accuracy: 29.56%
91
+ 2025-04-03 01:08:29,860 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
92
+ 2025-04-03 01:08:32,328 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
93
+ 2025-04-03 01:08:32,328 - INFO - Epoch 12/20
94
+ 2025-04-03 01:08:34,949 - INFO - Train Epoch: 12 [0/14 (0%)] Loss: 1.0527
95
+ 2025-04-03 01:08:52,151 - INFO - Train Epoch: 12 [10/14 (71%)] Loss: 0.9890
96
+ 2025-04-03 01:08:57,062 - INFO - Epoch 12 - Train Loss: 1.0674
97
+ 2025-04-03 01:09:42,461 - INFO - Epoch 12 - Validation Accuracy: 33.75%
98
+ 2025-04-03 01:09:42,462 - INFO - New best validation accuracy: 33.75%
99
+ 2025-04-03 01:09:44,260 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
100
+ 2025-04-03 01:09:46,163 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
101
+ 2025-04-03 01:09:46,163 - INFO - Epoch 13/20
102
+ 2025-04-03 01:09:48,770 - INFO - Train Epoch: 13 [0/14 (0%)] Loss: 0.9353
103
+ 2025-04-03 01:10:05,835 - INFO - Train Epoch: 13 [10/14 (71%)] Loss: 0.9083
104
+ 2025-04-03 01:10:10,697 - INFO - Epoch 13 - Train Loss: 0.9915
105
+ 2025-04-03 01:10:55,652 - INFO - Epoch 13 - Validation Accuracy: 37.20%
106
+ 2025-04-03 01:10:55,653 - INFO - New best validation accuracy: 37.20%
107
+ 2025-04-03 01:10:57,353 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
108
+ 2025-04-03 01:11:00,360 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
109
+ 2025-04-03 01:11:00,360 - INFO - Epoch 14/20
110
+ 2025-04-03 01:11:03,052 - INFO - Train Epoch: 14 [0/14 (0%)] Loss: 0.8993
111
+ 2025-04-03 01:11:20,012 - INFO - Train Epoch: 14 [10/14 (71%)] Loss: 0.9838
112
+ 2025-04-03 01:11:24,855 - INFO - Epoch 14 - Train Loss: 0.9328
113
+ 2025-04-03 01:12:09,262 - INFO - Epoch 14 - Validation Accuracy: 40.61%
114
+ 2025-04-03 01:12:09,262 - INFO - New best validation accuracy: 40.61%
115
+ 2025-04-03 01:12:11,066 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
116
+ 2025-04-03 01:12:12,815 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
117
+ 2025-04-03 01:12:12,816 - INFO - Epoch 15/20
118
+ 2025-04-03 01:12:15,448 - INFO - Train Epoch: 15 [0/14 (0%)] Loss: 0.7927
119
+ 2025-04-03 01:12:32,392 - INFO - Train Epoch: 15 [10/14 (71%)] Loss: 0.9750
120
+ 2025-04-03 01:12:37,227 - INFO - Epoch 15 - Train Loss: 0.8846
121
+ 2025-04-03 01:13:21,443 - INFO - Epoch 15 - Validation Accuracy: 43.44%
122
+ 2025-04-03 01:13:21,443 - INFO - New best validation accuracy: 43.44%
123
+ 2025-04-03 01:13:23,204 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
124
+ 2025-04-03 01:13:25,922 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
125
+ 2025-04-03 01:13:25,922 - INFO - Epoch 16/20
126
+ 2025-04-03 01:13:28,587 - INFO - Train Epoch: 16 [0/14 (0%)] Loss: 0.8614
127
+ 2025-04-03 01:13:45,524 - INFO - Train Epoch: 16 [10/14 (71%)] Loss: 0.7651
128
+ 2025-04-03 01:13:50,385 - INFO - Epoch 16 - Train Loss: 0.8286
129
+ 2025-04-03 01:14:34,468 - INFO - Epoch 16 - Validation Accuracy: 46.43%
130
+ 2025-04-03 01:14:34,468 - INFO - New best validation accuracy: 46.43%
131
+ 2025-04-03 01:14:36,191 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
132
+ 2025-04-03 01:14:37,956 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
133
+ 2025-04-03 01:14:37,956 - INFO - Epoch 17/20
134
+ 2025-04-03 01:14:40,539 - INFO - Train Epoch: 17 [0/14 (0%)] Loss: 0.7731
135
+ 2025-04-03 01:14:57,533 - INFO - Train Epoch: 17 [10/14 (71%)] Loss: 0.7870
136
+ 2025-04-03 01:15:02,390 - INFO - Epoch 17 - Train Loss: 0.8008
137
+ 2025-04-03 01:15:47,100 - INFO - Epoch 17 - Validation Accuracy: 47.85%
138
+ 2025-04-03 01:15:47,101 - INFO - New best validation accuracy: 47.85%
139
+ 2025-04-03 01:15:48,777 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
140
+ 2025-04-03 01:15:50,382 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
141
+ 2025-04-03 01:15:50,382 - INFO - Epoch 18/20
142
+ 2025-04-03 01:15:53,026 - INFO - Train Epoch: 18 [0/14 (0%)] Loss: 0.8760
143
+ 2025-04-03 01:16:09,947 - INFO - Train Epoch: 18 [10/14 (71%)] Loss: 0.6851
144
+ 2025-04-03 01:16:14,773 - INFO - Epoch 18 - Train Loss: 0.7412
145
+ 2025-04-03 01:16:59,043 - INFO - Epoch 18 - Validation Accuracy: 49.89%
146
+ 2025-04-03 01:16:59,043 - INFO - New best validation accuracy: 49.89%
147
+ 2025-04-03 01:17:00,797 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
148
+ 2025-04-03 01:17:02,503 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
149
+ 2025-04-03 01:17:02,504 - INFO - Epoch 19/20
150
+ 2025-04-03 01:17:05,125 - INFO - Train Epoch: 19 [0/14 (0%)] Loss: 0.7171
151
+ 2025-04-03 01:17:22,051 - INFO - Train Epoch: 19 [10/14 (71%)] Loss: 0.6547
152
+ 2025-04-03 01:17:26,888 - INFO - Epoch 19 - Train Loss: 0.7076
153
+ 2025-04-03 01:18:11,118 - INFO - Epoch 19 - Validation Accuracy: 51.86%
154
+ 2025-04-03 01:18:11,118 - INFO - New best validation accuracy: 51.86%
155
+ 2025-04-03 01:18:12,880 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
156
+ 2025-04-03 01:18:14,605 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
157
+ 2025-04-03 01:18:14,605 - INFO - Epoch 20/20
158
+ 2025-04-03 01:18:17,211 - INFO - Train Epoch: 20 [0/14 (0%)] Loss: 0.6143
159
+ 2025-04-03 01:18:34,127 - INFO - Train Epoch: 20 [10/14 (71%)] Loss: 0.6985
160
+ 2025-04-03 01:18:38,970 - INFO - Epoch 20 - Train Loss: 0.6661
161
+ 2025-04-03 01:19:23,150 - INFO - Epoch 20 - Validation Accuracy: 52.86%
162
+ 2025-04-03 01:19:23,150 - INFO - New best validation accuracy: 52.86%
163
+ 2025-04-03 01:19:24,754 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/best_model
164
+ 2025-04-03 01:19:26,605 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
165
+ 2025-04-03 01:20:11,062 - INFO - Final validation accuracy: 52.86%
166
+ 2025-04-03 01:20:11,063 - INFO - Best validation accuracy: 52.86%
167
+ 2025-04-03 01:20:13,663 - INFO - Checkpoint saved to outputs/exp2/vit/fewshot_10_percent/checkpoints/checkpoint_last
168
+ 2025-04-03 01:20:13,664 - INFO - Training completed
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