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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.7

Image Classification
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PyTorch
resnet
Generated from Trainer
Inference Endpoints
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metadata
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_a0.7
    results: []

resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.7

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: 0.8009
  • Accuracy: 0.695
  • Brier Loss: 0.4518
  • Nll: 2.3840
  • F1 Micro: 0.695
  • F1 Macro: 0.6406
  • Ece: 0.2661
  • Aurc: 0.1211

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 1.7971 0.17 0.8966 8.4593 0.17 0.1148 0.2202 0.8308
No log 2.0 26 1.7887 0.13 0.8956 8.3211 0.13 0.0772 0.2024 0.8359
No log 3.0 39 1.7450 0.225 0.8868 6.4554 0.225 0.1165 0.2502 0.7572
No log 4.0 52 1.6811 0.24 0.8733 5.9510 0.24 0.0953 0.2651 0.6944
No log 5.0 65 1.6411 0.19 0.8649 6.0993 0.19 0.0493 0.2422 0.7783
No log 6.0 78 1.5475 0.195 0.8429 6.2065 0.195 0.0630 0.2472 0.7110
No log 7.0 91 1.4688 0.3 0.8197 6.0345 0.3 0.1481 0.2936 0.5379
No log 8.0 104 1.5036 0.285 0.8294 5.6660 0.285 0.1428 0.2869 0.6535
No log 9.0 117 1.3901 0.34 0.7934 5.9107 0.34 0.1853 0.2894 0.5277
No log 10.0 130 1.3484 0.37 0.7760 5.6441 0.37 0.2175 0.3177 0.5266
No log 11.0 143 1.3375 0.34 0.7734 5.0872 0.34 0.2083 0.2902 0.5557
No log 12.0 156 1.3639 0.305 0.7834 4.5070 0.305 0.1885 0.2674 0.6177
No log 13.0 169 1.2321 0.415 0.7225 4.3464 0.415 0.2751 0.2943 0.3825
No log 14.0 182 1.1453 0.44 0.6767 4.4158 0.44 0.2864 0.2617 0.3413
No log 15.0 195 1.1830 0.43 0.6965 3.8251 0.4300 0.2972 0.2912 0.4239
No log 16.0 208 1.0572 0.535 0.6230 3.5943 0.535 0.3758 0.2861 0.2291
No log 17.0 221 1.0532 0.585 0.6151 3.3834 0.585 0.4331 0.3278 0.1879
No log 18.0 234 1.0940 0.565 0.6374 3.2290 0.565 0.4431 0.3313 0.2415
No log 19.0 247 0.9877 0.585 0.5886 3.1068 0.585 0.4564 0.2896 0.2110
No log 20.0 260 1.0405 0.61 0.6056 3.1786 0.61 0.5038 0.3428 0.1962
No log 21.0 273 0.9728 0.635 0.5634 2.9133 0.635 0.5293 0.3333 0.1664
No log 22.0 286 0.9425 0.635 0.5527 2.8909 0.635 0.5237 0.3131 0.1796
No log 23.0 299 0.9549 0.65 0.5605 2.8074 0.65 0.5539 0.3283 0.1914
No log 24.0 312 1.0085 0.67 0.5733 2.8377 0.67 0.5543 0.3525 0.1571
No log 25.0 325 0.9140 0.655 0.5257 2.5878 0.655 0.5603 0.3171 0.1495
No log 26.0 338 0.8979 0.65 0.5249 2.7723 0.65 0.5563 0.2843 0.1646
No log 27.0 351 0.8912 0.675 0.5082 2.6562 0.675 0.5837 0.2871 0.1380
No log 28.0 364 0.8966 0.66 0.5242 2.3150 0.66 0.5890 0.3180 0.1777
No log 29.0 377 0.8602 0.67 0.4959 2.5813 0.67 0.5866 0.3023 0.1319
No log 30.0 390 0.8434 0.69 0.4779 2.5451 0.69 0.6130 0.3061 0.1188
No log 31.0 403 0.8406 0.715 0.4782 2.3339 0.715 0.6438 0.3241 0.1092
No log 32.0 416 0.8294 0.71 0.4726 2.5394 0.7100 0.6308 0.2922 0.1218
No log 33.0 429 0.8329 0.68 0.4763 2.4520 0.68 0.6166 0.2592 0.1396
No log 34.0 442 0.8937 0.69 0.5015 2.5649 0.69 0.6357 0.3293 0.1279
No log 35.0 455 0.8358 0.665 0.4807 2.4437 0.665 0.6178 0.2380 0.1473
No log 36.0 468 0.8283 0.685 0.4747 2.5408 0.685 0.6304 0.3126 0.1361
No log 37.0 481 0.8235 0.685 0.4707 2.4620 0.685 0.6300 0.2757 0.1343
No log 38.0 494 0.8289 0.68 0.4778 2.5443 0.68 0.6305 0.2935 0.1469
0.9462 39.0 507 0.8373 0.69 0.4728 2.5775 0.69 0.6281 0.3028 0.1149
0.9462 40.0 520 0.8062 0.715 0.4548 2.3673 0.715 0.6587 0.2776 0.1133
0.9462 41.0 533 0.7990 0.705 0.4517 2.3284 0.705 0.6463 0.2716 0.1185
0.9462 42.0 546 0.8210 0.7 0.4650 2.5646 0.7 0.6432 0.2690 0.1199
0.9462 43.0 559 0.8102 0.695 0.4558 2.5651 0.695 0.6442 0.2656 0.1184
0.9462 44.0 572 0.8061 0.69 0.4566 2.5154 0.69 0.6356 0.2816 0.1267
0.9462 45.0 585 0.8018 0.7 0.4531 2.4982 0.7 0.6419 0.2696 0.1192
0.9462 46.0 598 0.8040 0.7 0.4521 2.5309 0.7 0.6448 0.2797 0.1166
0.9462 47.0 611 0.8062 0.68 0.4560 2.5452 0.68 0.6370 0.2744 0.1217
0.9462 48.0 624 0.8011 0.69 0.4529 2.4281 0.69 0.6402 0.2594 0.1224
0.9462 49.0 637 0.8017 0.69 0.4532 2.4239 0.69 0.6400 0.2613 0.1261
0.9462 50.0 650 0.8009 0.695 0.4518 2.3840 0.695 0.6406 0.2661 0.1211

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.2.0.dev20231002
  • Datasets 2.7.1
  • Tokenizers 0.13.3