--- 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_t1.5_a0.9 results: [] --- # resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.9 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: 0.8831 - Accuracy: 0.695 - Brier Loss: 0.4126 - Nll: 2.4628 - F1 Micro: 0.695 - F1 Macro: 0.6387 - Ece: 0.2426 - Aurc: 0.1068 ## 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 | 2.1233 | 0.16 | 0.8967 | 8.5697 | 0.16 | 0.1066 | 0.2078 | 0.8316 | | No log | 2.0 | 26 | 2.1188 | 0.14 | 0.8961 | 8.2960 | 0.14 | 0.0886 | 0.1947 | 0.8419 | | No log | 3.0 | 39 | 2.0764 | 0.195 | 0.8873 | 6.4713 | 0.195 | 0.1159 | 0.2335 | 0.7665 | | No log | 4.0 | 52 | 2.0365 | 0.21 | 0.8787 | 5.7752 | 0.2100 | 0.0930 | 0.2376 | 0.7548 | | No log | 5.0 | 65 | 1.9888 | 0.2 | 0.8682 | 5.8737 | 0.2000 | 0.0775 | 0.2417 | 0.7314 | | No log | 6.0 | 78 | 1.8998 | 0.215 | 0.8465 | 5.8553 | 0.2150 | 0.0970 | 0.2586 | 0.7063 | | No log | 7.0 | 91 | 1.8351 | 0.33 | 0.8289 | 5.7781 | 0.33 | 0.1904 | 0.3089 | 0.6103 | | No log | 8.0 | 104 | 1.7342 | 0.4 | 0.7968 | 5.5366 | 0.4000 | 0.2476 | 0.3457 | 0.4276 | | No log | 9.0 | 117 | 1.6787 | 0.36 | 0.7757 | 5.7414 | 0.36 | 0.2148 | 0.3062 | 0.4324 | | No log | 10.0 | 130 | 1.6942 | 0.4 | 0.7870 | 5.2615 | 0.4000 | 0.2831 | 0.3168 | 0.5227 | | No log | 11.0 | 143 | 1.5992 | 0.4 | 0.7489 | 4.7833 | 0.4000 | 0.2649 | 0.3053 | 0.4679 | | No log | 12.0 | 156 | 1.6071 | 0.425 | 0.7532 | 4.2803 | 0.425 | 0.2906 | 0.3196 | 0.4646 | | No log | 13.0 | 169 | 1.4727 | 0.48 | 0.6925 | 4.1911 | 0.48 | 0.3239 | 0.2957 | 0.3081 | | No log | 14.0 | 182 | 1.4275 | 0.515 | 0.6705 | 3.7980 | 0.515 | 0.3569 | 0.3211 | 0.2626 | | No log | 15.0 | 195 | 1.3282 | 0.56 | 0.6200 | 3.6359 | 0.56 | 0.4163 | 0.2990 | 0.2213 | | No log | 16.0 | 208 | 1.3280 | 0.565 | 0.6263 | 3.4960 | 0.565 | 0.4177 | 0.3217 | 0.2346 | | No log | 17.0 | 221 | 1.3220 | 0.595 | 0.6196 | 3.2202 | 0.595 | 0.4639 | 0.3322 | 0.1992 | | No log | 18.0 | 234 | 1.2359 | 0.595 | 0.5840 | 3.3332 | 0.595 | 0.4780 | 0.3042 | 0.2011 | | No log | 19.0 | 247 | 1.1690 | 0.625 | 0.5531 | 3.2423 | 0.625 | 0.5233 | 0.2940 | 0.1807 | | No log | 20.0 | 260 | 1.1644 | 0.64 | 0.5532 | 3.0542 | 0.64 | 0.5429 | 0.3019 | 0.1821 | | No log | 21.0 | 273 | 1.1611 | 0.62 | 0.5516 | 2.9412 | 0.62 | 0.5193 | 0.2865 | 0.2160 | | No log | 22.0 | 286 | 1.3427 | 0.585 | 0.6361 | 3.0936 | 0.585 | 0.5089 | 0.3442 | 0.2922 | | No log | 23.0 | 299 | 1.1238 | 0.62 | 0.5440 | 2.7924 | 0.62 | 0.5458 | 0.2654 | 0.2088 | | No log | 24.0 | 312 | 1.2008 | 0.685 | 0.5615 | 2.5918 | 0.685 | 0.5890 | 0.3907 | 0.1516 | | No log | 25.0 | 325 | 1.0764 | 0.695 | 0.5000 | 2.6354 | 0.695 | 0.6107 | 0.3126 | 0.1397 | | No log | 26.0 | 338 | 1.0268 | 0.675 | 0.4822 | 2.4798 | 0.675 | 0.5992 | 0.2775 | 0.1229 | | No log | 27.0 | 351 | 1.0340 | 0.67 | 0.4893 | 2.4316 | 0.67 | 0.5997 | 0.2763 | 0.1638 | | No log | 28.0 | 364 | 1.0154 | 0.665 | 0.4769 | 2.6487 | 0.665 | 0.6034 | 0.2590 | 0.1487 | | No log | 29.0 | 377 | 1.0013 | 0.64 | 0.4814 | 2.5899 | 0.64 | 0.5771 | 0.2429 | 0.1593 | | No log | 30.0 | 390 | 1.0173 | 0.685 | 0.4714 | 2.6922 | 0.685 | 0.6178 | 0.2898 | 0.1423 | | No log | 31.0 | 403 | 0.9630 | 0.695 | 0.4509 | 2.6349 | 0.695 | 0.6206 | 0.2746 | 0.1248 | | No log | 32.0 | 416 | 0.9950 | 0.68 | 0.4648 | 2.4144 | 0.68 | 0.6362 | 0.3020 | 0.1725 | | No log | 33.0 | 429 | 0.9711 | 0.72 | 0.4502 | 2.6651 | 0.72 | 0.6571 | 0.2892 | 0.1268 | | No log | 34.0 | 442 | 0.9491 | 0.705 | 0.4425 | 2.7169 | 0.705 | 0.6425 | 0.2541 | 0.1145 | | No log | 35.0 | 455 | 0.9213 | 0.685 | 0.4309 | 2.5736 | 0.685 | 0.6174 | 0.2380 | 0.1161 | | No log | 36.0 | 468 | 0.9144 | 0.695 | 0.4297 | 2.4141 | 0.695 | 0.6308 | 0.2502 | 0.1154 | | No log | 37.0 | 481 | 0.9242 | 0.715 | 0.4264 | 2.7191 | 0.715 | 0.6429 | 0.2386 | 0.1030 | | No log | 38.0 | 494 | 0.9290 | 0.695 | 0.4346 | 2.6515 | 0.695 | 0.6367 | 0.2432 | 0.1189 | | 1.0953 | 39.0 | 507 | 0.9110 | 0.69 | 0.4262 | 2.6615 | 0.69 | 0.6328 | 0.2368 | 0.1112 | | 1.0953 | 40.0 | 520 | 0.9000 | 0.695 | 0.4186 | 2.4590 | 0.695 | 0.6417 | 0.2453 | 0.1070 | | 1.0953 | 41.0 | 533 | 0.8961 | 0.69 | 0.4189 | 2.4170 | 0.69 | 0.6368 | 0.2349 | 0.1090 | | 1.0953 | 42.0 | 546 | 0.9103 | 0.675 | 0.4286 | 2.6129 | 0.675 | 0.6193 | 0.2318 | 0.1190 | | 1.0953 | 43.0 | 559 | 0.8858 | 0.715 | 0.4131 | 2.5243 | 0.715 | 0.6517 | 0.2462 | 0.1053 | | 1.0953 | 44.0 | 572 | 0.8872 | 0.705 | 0.4135 | 2.3272 | 0.705 | 0.6542 | 0.2596 | 0.1051 | | 1.0953 | 45.0 | 585 | 0.8897 | 0.715 | 0.4136 | 2.3788 | 0.715 | 0.6532 | 0.2560 | 0.1035 | | 1.0953 | 46.0 | 598 | 0.8842 | 0.7 | 0.4117 | 2.5325 | 0.7 | 0.6446 | 0.2327 | 0.1075 | | 1.0953 | 47.0 | 611 | 0.8857 | 0.675 | 0.4141 | 2.5451 | 0.675 | 0.6203 | 0.2473 | 0.1125 | | 1.0953 | 48.0 | 624 | 0.8875 | 0.69 | 0.4164 | 2.4696 | 0.69 | 0.6352 | 0.2542 | 0.1109 | | 1.0953 | 49.0 | 637 | 0.8842 | 0.69 | 0.4153 | 2.5338 | 0.69 | 0.6358 | 0.2302 | 0.1112 | | 1.0953 | 50.0 | 650 | 0.8831 | 0.695 | 0.4126 | 2.4628 | 0.695 | 0.6387 | 0.2426 | 0.1068 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0.dev20231002 - Datasets 2.7.1 - Tokenizers 0.13.3