resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.5
This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6500
- Accuracy: 0.69
- Brier Loss: 0.5003
- Nll: 2.5629
- F1 Micro: 0.69
- F1 Macro: 0.6350
- Ece: 0.3098
- Aurc: 0.1329
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 13 | 1.4712 | 0.165 | 0.8966 | 8.4652 | 0.165 | 0.1101 | 0.2129 | 0.8342 |
No log | 2.0 | 26 | 1.4590 | 0.165 | 0.8951 | 8.1097 | 0.165 | 0.1059 | 0.2034 | 0.8021 |
No log | 3.0 | 39 | 1.4178 | 0.175 | 0.8873 | 6.8095 | 0.175 | 0.0813 | 0.2150 | 0.7994 |
No log | 4.0 | 52 | 1.3342 | 0.18 | 0.8702 | 6.4137 | 0.18 | 0.0475 | 0.2314 | 0.7558 |
No log | 5.0 | 65 | 1.2828 | 0.2 | 0.8587 | 6.1547 | 0.2000 | 0.0642 | 0.2429 | 0.7009 |
No log | 6.0 | 78 | 1.2675 | 0.205 | 0.8548 | 6.1395 | 0.205 | 0.0612 | 0.2348 | 0.7022 |
No log | 7.0 | 91 | 1.0716 | 0.31 | 0.7962 | 6.4589 | 0.31 | 0.1241 | 0.2787 | 0.4433 |
No log | 8.0 | 104 | 1.1184 | 0.29 | 0.8126 | 6.2585 | 0.29 | 0.1394 | 0.2863 | 0.5819 |
No log | 9.0 | 117 | 1.1021 | 0.31 | 0.8075 | 6.0370 | 0.31 | 0.1697 | 0.2834 | 0.5458 |
No log | 10.0 | 130 | 1.0268 | 0.33 | 0.7815 | 6.1370 | 0.33 | 0.1921 | 0.2856 | 0.5395 |
No log | 11.0 | 143 | 1.0290 | 0.355 | 0.7759 | 5.3640 | 0.3550 | 0.2143 | 0.2795 | 0.4697 |
No log | 12.0 | 156 | 0.9169 | 0.36 | 0.7262 | 5.2997 | 0.36 | 0.1995 | 0.2761 | 0.4070 |
No log | 13.0 | 169 | 0.9903 | 0.36 | 0.7586 | 4.9404 | 0.36 | 0.2200 | 0.2832 | 0.5343 |
No log | 14.0 | 182 | 0.9128 | 0.425 | 0.7082 | 4.5862 | 0.425 | 0.2706 | 0.2834 | 0.3542 |
No log | 15.0 | 195 | 1.0046 | 0.405 | 0.7441 | 3.9763 | 0.405 | 0.2759 | 0.3142 | 0.4602 |
No log | 16.0 | 208 | 0.9277 | 0.41 | 0.7146 | 4.3670 | 0.41 | 0.2763 | 0.2695 | 0.4409 |
No log | 17.0 | 221 | 0.9726 | 0.505 | 0.7208 | 3.5350 | 0.505 | 0.3736 | 0.3332 | 0.3469 |
No log | 18.0 | 234 | 0.7717 | 0.505 | 0.6280 | 3.4386 | 0.505 | 0.3412 | 0.2564 | 0.2567 |
No log | 19.0 | 247 | 0.7723 | 0.58 | 0.6143 | 3.6207 | 0.58 | 0.4125 | 0.3178 | 0.1847 |
No log | 20.0 | 260 | 0.8182 | 0.57 | 0.6419 | 3.1633 | 0.57 | 0.4855 | 0.3517 | 0.2530 |
No log | 21.0 | 273 | 0.7333 | 0.58 | 0.5891 | 3.3014 | 0.58 | 0.4512 | 0.2718 | 0.2137 |
No log | 22.0 | 286 | 0.7374 | 0.665 | 0.5856 | 3.0299 | 0.665 | 0.5432 | 0.3459 | 0.1657 |
No log | 23.0 | 299 | 0.7083 | 0.645 | 0.5564 | 3.0874 | 0.645 | 0.5180 | 0.3112 | 0.1608 |
No log | 24.0 | 312 | 0.7480 | 0.64 | 0.5901 | 3.0218 | 0.64 | 0.5410 | 0.3701 | 0.1976 |
No log | 25.0 | 325 | 0.7547 | 0.68 | 0.5894 | 2.9002 | 0.68 | 0.5801 | 0.3817 | 0.1559 |
No log | 26.0 | 338 | 0.6998 | 0.65 | 0.5474 | 2.9402 | 0.65 | 0.5468 | 0.2875 | 0.1707 |
No log | 27.0 | 351 | 0.6967 | 0.66 | 0.5506 | 2.8344 | 0.66 | 0.5578 | 0.3105 | 0.1707 |
No log | 28.0 | 364 | 0.6733 | 0.655 | 0.5332 | 2.6492 | 0.655 | 0.5719 | 0.2935 | 0.1554 |
No log | 29.0 | 377 | 0.7162 | 0.67 | 0.5596 | 2.7250 | 0.67 | 0.5721 | 0.3388 | 0.1423 |
No log | 30.0 | 390 | 0.6826 | 0.665 | 0.5291 | 2.7460 | 0.665 | 0.5797 | 0.3353 | 0.1469 |
No log | 31.0 | 403 | 0.6761 | 0.665 | 0.5195 | 2.7938 | 0.665 | 0.5647 | 0.3096 | 0.1485 |
No log | 32.0 | 416 | 0.6745 | 0.695 | 0.5295 | 2.6172 | 0.695 | 0.6160 | 0.3171 | 0.1636 |
No log | 33.0 | 429 | 0.6785 | 0.695 | 0.5242 | 2.5816 | 0.695 | 0.6115 | 0.3475 | 0.1349 |
No log | 34.0 | 442 | 0.6688 | 0.665 | 0.5174 | 2.6401 | 0.665 | 0.5833 | 0.2988 | 0.1427 |
No log | 35.0 | 455 | 0.6767 | 0.675 | 0.5275 | 2.6364 | 0.675 | 0.6027 | 0.3285 | 0.1483 |
No log | 36.0 | 468 | 0.6605 | 0.695 | 0.5076 | 2.6483 | 0.695 | 0.6252 | 0.3127 | 0.1372 |
No log | 37.0 | 481 | 0.6538 | 0.705 | 0.5029 | 2.6284 | 0.705 | 0.6340 | 0.3173 | 0.1220 |
No log | 38.0 | 494 | 0.6610 | 0.695 | 0.5102 | 2.5052 | 0.695 | 0.6375 | 0.3128 | 0.1298 |
0.7532 | 39.0 | 507 | 0.6618 | 0.695 | 0.5110 | 2.5663 | 0.695 | 0.6268 | 0.3297 | 0.1367 |
0.7532 | 40.0 | 520 | 0.6749 | 0.69 | 0.5235 | 2.5343 | 0.69 | 0.6341 | 0.3256 | 0.1332 |
0.7532 | 41.0 | 533 | 0.6574 | 0.695 | 0.5062 | 2.4223 | 0.695 | 0.6338 | 0.3292 | 0.1469 |
0.7532 | 42.0 | 546 | 0.6530 | 0.695 | 0.5026 | 2.6189 | 0.695 | 0.6390 | 0.2950 | 0.1391 |
0.7532 | 43.0 | 559 | 0.6509 | 0.685 | 0.5003 | 2.5417 | 0.685 | 0.6299 | 0.3150 | 0.1368 |
0.7532 | 44.0 | 572 | 0.6520 | 0.71 | 0.5030 | 2.4796 | 0.7100 | 0.6453 | 0.3251 | 0.1286 |
0.7532 | 45.0 | 585 | 0.6494 | 0.69 | 0.4994 | 2.5431 | 0.69 | 0.6327 | 0.3138 | 0.1279 |
0.7532 | 46.0 | 598 | 0.6515 | 0.71 | 0.5007 | 2.5295 | 0.7100 | 0.6541 | 0.3307 | 0.1208 |
0.7532 | 47.0 | 611 | 0.6477 | 0.69 | 0.4979 | 2.5971 | 0.69 | 0.6323 | 0.3263 | 0.1281 |
0.7532 | 48.0 | 624 | 0.6495 | 0.7 | 0.5007 | 2.6162 | 0.7 | 0.6395 | 0.3412 | 0.1272 |
0.7532 | 49.0 | 637 | 0.6478 | 0.7 | 0.4968 | 2.4946 | 0.7 | 0.6386 | 0.3191 | 0.1309 |
0.7532 | 50.0 | 650 | 0.6500 | 0.69 | 0.5003 | 2.5629 | 0.69 | 0.6350 | 0.3098 | 0.1329 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
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Model tree for bdpc/resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.5
Base model
microsoft/resnet-50
Finetuned
this model