generated_from_trainer

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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_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:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 1.0 13 1.4475 0.17 0.8966 8.4781 0.17 0.1126 0.2169 0.8299
No log 2.0 26 1.4360 0.165 0.8955 8.4118 0.165 0.1097 0.2115 0.8359
No log 3.0 39 1.3776 0.16 0.8842 6.1685 0.16 0.0633 0.2066 0.7780
No log 4.0 52 1.3085 0.2 0.8701 6.0521 0.2000 0.0728 0.2332 0.7424
No log 5.0 65 1.2551 0.18 0.8597 6.1887 0.18 0.0491 0.2265 0.7890
No log 6.0 78 1.2118 0.2 0.8489 6.1706 0.2000 0.0631 0.2324 0.7179
No log 7.0 91 1.1759 0.19 0.8418 6.1310 0.19 0.0428 0.2384 0.7431
No log 8.0 104 1.1577 0.195 0.8339 5.7305 0.195 0.0535 0.2399 0.7126
No log 9.0 117 0.9905 0.34 0.7692 6.1092 0.34 0.1567 0.2772 0.4320
No log 10.0 130 0.9603 0.355 0.7541 5.7998 0.3550 0.1969 0.3021 0.4111
No log 11.0 143 1.0839 0.255 0.8087 5.1464 0.255 0.1242 0.2389 0.6769
No log 12.0 156 0.9374 0.39 0.7410 4.8415 0.39 0.2220 0.3037 0.4194
No log 13.0 169 0.9974 0.33 0.7720 4.9023 0.33 0.1732 0.2863 0.6049
No log 14.0 182 0.9393 0.435 0.7251 4.3102 0.435 0.2455 0.3276 0.3645
No log 15.0 195 0.9554 0.39 0.7416 4.0361 0.39 0.2535 0.2721 0.5075
No log 16.0 208 0.8012 0.465 0.6445 4.0129 0.465 0.2935 0.2551 0.2839
No log 17.0 221 0.8033 0.53 0.6418 3.4959 0.53 0.3816 0.3242 0.2458
No log 18.0 234 0.7740 0.57 0.6204 3.4062 0.57 0.4297 0.3139 0.2245
No log 19.0 247 0.7736 0.6 0.6124 3.3460 0.6 0.4408 0.3017 0.1919
No log 20.0 260 0.9105 0.555 0.6919 3.2115 0.555 0.4524 0.3604 0.3099
No log 21.0 273 0.7416 0.61 0.5948 3.1349 0.61 0.5093 0.3176 0.2233
No log 22.0 286 0.7318 0.655 0.5815 3.1259 0.655 0.5433 0.3478 0.1672
No log 23.0 299 0.7799 0.59 0.6079 3.0590 0.59 0.4963 0.3340 0.2455
No log 24.0 312 0.7886 0.665 0.6038 2.9965 0.665 0.5575 0.3773 0.1623
No log 25.0 325 0.7083 0.66 0.5602 3.0752 0.66 0.5582 0.3283 0.1772
No log 26.0 338 0.6882 0.63 0.5507 2.9022 0.63 0.5404 0.2963 0.1851
No log 27.0 351 0.6774 0.66 0.5348 2.7876 0.66 0.5674 0.3095 0.1662
No log 28.0 364 0.8111 0.675 0.6067 2.7578 0.675 0.5800 0.3905 0.1923
No log 29.0 377 0.6803 0.645 0.5338 2.8666 0.645 0.5486 0.3054 0.1646
No log 30.0 390 0.6835 0.685 0.5336 2.5944 0.685 0.5840 0.3119 0.1595
No log 31.0 403 0.6810 0.655 0.5309 2.7112 0.655 0.5625 0.2879 0.1786
No log 32.0 416 0.6848 0.685 0.5194 2.6456 0.685 0.5893 0.3314 0.1350
No log 33.0 429 0.6631 0.695 0.5063 2.6286 0.695 0.5980 0.3198 0.1314
No log 34.0 442 0.6639 0.69 0.5126 2.3890 0.69 0.5834 0.2990 0.1376
No log 35.0 455 0.6736 0.675 0.5172 2.3291 0.675 0.6014 0.3148 0.1646
No log 36.0 468 0.6648 0.68 0.5137 2.4549 0.68 0.6156 0.3316 0.1492
No log 37.0 481 0.6543 0.7 0.5006 2.4275 0.7 0.6130 0.3041 0.1342
No log 38.0 494 0.6514 0.675 0.5001 2.4064 0.675 0.5984 0.2963 0.1491
0.7462 39.0 507 0.6498 0.71 0.4988 2.5772 0.7100 0.6405 0.2980 0.1335
0.7462 40.0 520 0.6496 0.705 0.4964 2.5649 0.705 0.6386 0.3060 0.1380
0.7462 41.0 533 0.6562 0.68 0.5027 2.5816 0.68 0.6026 0.3100 0.1467
0.7462 42.0 546 0.6632 0.68 0.5089 2.4570 0.68 0.6112 0.2989 0.1500
0.7462 43.0 559 0.6437 0.7 0.4885 2.3648 0.7 0.6331 0.2741 0.1427
0.7462 44.0 572 0.6435 0.705 0.4894 2.4253 0.705 0.6370 0.3043 0.1390
0.7462 45.0 585 0.6457 0.695 0.4929 2.3611 0.695 0.6314 0.3021 0.1449
0.7462 46.0 598 0.6437 0.695 0.4912 2.3639 0.695 0.6370 0.2984 0.1436
0.7462 47.0 611 0.6466 0.685 0.4933 2.4859 0.685 0.6306 0.2936 0.1474
0.7462 48.0 624 0.6470 0.67 0.4950 2.3782 0.67 0.6070 0.3139 0.1547
0.7462 49.0 637 0.6477 0.675 0.4945 2.4509 0.675 0.6092 0.2852 0.1527
0.7462 50.0 650 0.6454 0.685 0.4931 2.5040 0.685 0.6171 0.2996 0.1499

Framework versions