--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer metrics: - accuracy model-index: - name: resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.5 results: [] --- # resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6481 - Accuracy: 0.69 - Brier Loss: 0.4919 - Nll: 2.4969 - F1 Micro: 0.69 - F1 Macro: 0.6317 - Ece: 0.3029 - Aurc: 0.1260 ## 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 | 1.4796 | 0.165 | 0.8965 | 8.4885 | 0.165 | 0.1123 | 0.2151 | 0.8341 | | No log | 2.0 | 26 | 1.4679 | 0.165 | 0.8954 | 8.3391 | 0.165 | 0.1066 | 0.2136 | 0.8332 | | No log | 3.0 | 39 | 1.4170 | 0.21 | 0.8858 | 6.1941 | 0.2100 | 0.0969 | 0.2433 | 0.7991 | | No log | 4.0 | 52 | 1.3472 | 0.21 | 0.8711 | 6.0602 | 0.2100 | 0.0728 | 0.2320 | 0.7271 | | No log | 5.0 | 65 | 1.2776 | 0.19 | 0.8572 | 6.1293 | 0.19 | 0.0537 | 0.2422 | 0.7473 | | No log | 6.0 | 78 | 1.1840 | 0.245 | 0.8353 | 6.2405 | 0.245 | 0.1060 | 0.2810 | 0.6690 | | No log | 7.0 | 91 | 1.0740 | 0.365 | 0.7936 | 6.3617 | 0.3650 | 0.1739 | 0.3136 | 0.3646 | | No log | 8.0 | 104 | 1.1102 | 0.345 | 0.8081 | 5.8896 | 0.345 | 0.1812 | 0.3046 | 0.4292 | | No log | 9.0 | 117 | 1.0735 | 0.34 | 0.7963 | 5.9970 | 0.34 | 0.1842 | 0.3028 | 0.4286 | | No log | 10.0 | 130 | 1.1145 | 0.265 | 0.8110 | 5.9054 | 0.265 | 0.1300 | 0.2511 | 0.6350 | | No log | 11.0 | 143 | 0.9981 | 0.325 | 0.7659 | 5.3834 | 0.325 | 0.1655 | 0.2790 | 0.4860 | | No log | 12.0 | 156 | 1.0500 | 0.285 | 0.7898 | 4.9696 | 0.285 | 0.1594 | 0.2604 | 0.6636 | | No log | 13.0 | 169 | 0.8764 | 0.445 | 0.6976 | 4.6456 | 0.445 | 0.2647 | 0.2779 | 0.3020 | | No log | 14.0 | 182 | 0.9147 | 0.48 | 0.7108 | 4.4793 | 0.48 | 0.2942 | 0.3262 | 0.2862 | | No log | 15.0 | 195 | 0.9776 | 0.38 | 0.7434 | 4.4065 | 0.38 | 0.2269 | 0.2938 | 0.5297 | | No log | 16.0 | 208 | 0.8066 | 0.47 | 0.6494 | 3.9671 | 0.47 | 0.2966 | 0.2791 | 0.2907 | | No log | 17.0 | 221 | 0.7766 | 0.535 | 0.6305 | 3.5250 | 0.535 | 0.3866 | 0.3003 | 0.2424 | | No log | 18.0 | 234 | 0.8186 | 0.535 | 0.6458 | 3.3670 | 0.535 | 0.3792 | 0.3005 | 0.2311 | | No log | 19.0 | 247 | 0.8156 | 0.52 | 0.6430 | 3.1633 | 0.52 | 0.3675 | 0.3072 | 0.2667 | | No log | 20.0 | 260 | 0.8386 | 0.55 | 0.6462 | 3.2549 | 0.55 | 0.4251 | 0.3103 | 0.2703 | | No log | 21.0 | 273 | 0.7996 | 0.515 | 0.6342 | 3.1396 | 0.515 | 0.3969 | 0.3177 | 0.2867 | | No log | 22.0 | 286 | 0.8605 | 0.6 | 0.6472 | 3.2563 | 0.6 | 0.4717 | 0.3810 | 0.2113 | | No log | 23.0 | 299 | 0.7138 | 0.595 | 0.5713 | 3.1171 | 0.595 | 0.4657 | 0.2773 | 0.2034 | | No log | 24.0 | 312 | 0.7212 | 0.665 | 0.5740 | 2.9688 | 0.665 | 0.5474 | 0.3366 | 0.1754 | | No log | 25.0 | 325 | 0.7463 | 0.63 | 0.5843 | 2.8998 | 0.63 | 0.5502 | 0.3432 | 0.2072 | | No log | 26.0 | 338 | 0.7231 | 0.67 | 0.5626 | 3.1334 | 0.67 | 0.5564 | 0.3160 | 0.1521 | | No log | 27.0 | 351 | 0.6913 | 0.68 | 0.5427 | 2.8906 | 0.68 | 0.5702 | 0.3354 | 0.1406 | | No log | 28.0 | 364 | 0.6825 | 0.66 | 0.5342 | 2.8619 | 0.66 | 0.5615 | 0.2902 | 0.1625 | | No log | 29.0 | 377 | 0.7015 | 0.665 | 0.5549 | 2.7315 | 0.665 | 0.5741 | 0.3305 | 0.1769 | | No log | 30.0 | 390 | 0.6939 | 0.67 | 0.5406 | 2.7114 | 0.67 | 0.5720 | 0.3353 | 0.1420 | | No log | 31.0 | 403 | 0.6836 | 0.69 | 0.5265 | 2.7567 | 0.69 | 0.5982 | 0.3216 | 0.1455 | | No log | 32.0 | 416 | 0.6728 | 0.69 | 0.5211 | 2.6858 | 0.69 | 0.6056 | 0.3124 | 0.1453 | | No log | 33.0 | 429 | 0.6926 | 0.675 | 0.5403 | 2.5815 | 0.675 | 0.6095 | 0.3258 | 0.1683 | | No log | 34.0 | 442 | 0.6673 | 0.66 | 0.5090 | 2.5591 | 0.66 | 0.5722 | 0.2950 | 0.1385 | | No log | 35.0 | 455 | 0.6811 | 0.675 | 0.5207 | 2.5813 | 0.675 | 0.5841 | 0.3324 | 0.1273 | | No log | 36.0 | 468 | 0.6648 | 0.69 | 0.5119 | 2.5745 | 0.69 | 0.6225 | 0.3433 | 0.1320 | | No log | 37.0 | 481 | 0.6623 | 0.67 | 0.5092 | 2.6134 | 0.67 | 0.6129 | 0.3204 | 0.1471 | | No log | 38.0 | 494 | 0.6635 | 0.69 | 0.5088 | 2.3862 | 0.69 | 0.6192 | 0.3201 | 0.1311 | | 0.7628 | 39.0 | 507 | 0.6554 | 0.685 | 0.5008 | 2.5849 | 0.685 | 0.6210 | 0.3179 | 0.1377 | | 0.7628 | 40.0 | 520 | 0.6567 | 0.685 | 0.5022 | 2.6498 | 0.685 | 0.6310 | 0.3127 | 0.1414 | | 0.7628 | 41.0 | 533 | 0.6558 | 0.695 | 0.4996 | 2.5917 | 0.695 | 0.6347 | 0.3115 | 0.1321 | | 0.7628 | 42.0 | 546 | 0.6578 | 0.695 | 0.5021 | 2.4864 | 0.695 | 0.6259 | 0.3098 | 0.1306 | | 0.7628 | 43.0 | 559 | 0.6544 | 0.685 | 0.4969 | 2.5757 | 0.685 | 0.6175 | 0.2955 | 0.1342 | | 0.7628 | 44.0 | 572 | 0.6507 | 0.685 | 0.4944 | 2.5057 | 0.685 | 0.6257 | 0.3144 | 0.1304 | | 0.7628 | 45.0 | 585 | 0.6501 | 0.675 | 0.4937 | 2.4903 | 0.675 | 0.6208 | 0.3091 | 0.1301 | | 0.7628 | 46.0 | 598 | 0.6518 | 0.685 | 0.4949 | 2.4732 | 0.685 | 0.6254 | 0.3164 | 0.1235 | | 0.7628 | 47.0 | 611 | 0.6499 | 0.685 | 0.4936 | 2.4924 | 0.685 | 0.6273 | 0.3124 | 0.1323 | | 0.7628 | 48.0 | 624 | 0.6490 | 0.7 | 0.4925 | 2.4999 | 0.7 | 0.6353 | 0.3147 | 0.1243 | | 0.7628 | 49.0 | 637 | 0.6510 | 0.685 | 0.4933 | 2.5758 | 0.685 | 0.6242 | 0.3206 | 0.1281 | | 0.7628 | 50.0 | 650 | 0.6481 | 0.69 | 0.4919 | 2.4969 | 0.69 | 0.6317 | 0.3029 | 0.1260 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3