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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.9

Image Classification
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PyTorch
resnet
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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_t2.5_a0.9
    results: []

resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.9

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: 0.8672
  • Accuracy: 0.71
  • Brier Loss: 0.4047
  • Nll: 2.1924
  • F1 Micro: 0.7100
  • F1 Macro: 0.6463
  • Ece: 0.2420
  • Aurc: 0.1050

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 2.1239 0.16 0.8967 8.4233 0.16 0.1062 0.2101 0.8304
No log 2.0 26 2.1201 0.14 0.8961 8.2220 0.14 0.0876 0.1970 0.8491
No log 3.0 39 2.0724 0.215 0.8865 6.2039 0.2150 0.1169 0.2432 0.7837
No log 4.0 52 2.0291 0.185 0.8773 5.6169 0.185 0.0792 0.2329 0.7651
No log 5.0 65 1.9592 0.215 0.8614 6.0237 0.2150 0.0835 0.2493 0.7373
No log 6.0 78 1.9039 0.205 0.8483 5.9575 0.205 0.0619 0.2493 0.7526
No log 7.0 91 1.8651 0.26 0.8381 5.6215 0.26 0.1490 0.2663 0.6747
No log 8.0 104 1.8342 0.225 0.8311 5.7631 0.225 0.1071 0.2425 0.6919
No log 9.0 117 1.8057 0.31 0.8218 5.2969 0.31 0.2118 0.2795 0.6489
No log 10.0 130 1.5737 0.46 0.7277 5.1748 0.46 0.2853 0.3279 0.2977
No log 11.0 143 1.5629 0.415 0.7331 4.8259 0.415 0.2846 0.2924 0.3880
No log 12.0 156 1.5283 0.45 0.7135 4.0012 0.45 0.3122 0.3298 0.3197
No log 13.0 169 1.4200 0.51 0.6674 3.9849 0.51 0.3400 0.3259 0.2549
No log 14.0 182 1.4334 0.535 0.6710 3.7006 0.535 0.3840 0.3291 0.2584
No log 15.0 195 1.4306 0.45 0.6854 3.8260 0.45 0.3120 0.3055 0.4297
No log 16.0 208 1.3175 0.585 0.6174 3.3484 0.585 0.4401 0.3406 0.1916
No log 17.0 221 1.2680 0.57 0.5998 3.1408 0.57 0.4356 0.2903 0.2136
No log 18.0 234 1.2605 0.58 0.6020 3.2085 0.58 0.4711 0.2915 0.2355
No log 19.0 247 1.2292 0.61 0.5791 3.0633 0.61 0.5021 0.2929 0.2082
No log 20.0 260 1.3872 0.54 0.6604 3.2778 0.54 0.4604 0.3284 0.3506
No log 21.0 273 1.1646 0.625 0.5520 2.8539 0.625 0.5193 0.2828 0.1885
No log 22.0 286 1.1565 0.655 0.5438 2.6915 0.655 0.5437 0.3430 0.1549
No log 23.0 299 1.1041 0.625 0.5298 2.9930 0.625 0.5241 0.2423 0.1906
No log 24.0 312 1.0448 0.685 0.4895 2.8196 0.685 0.5846 0.2701 0.1411
No log 25.0 325 1.0623 0.695 0.4904 2.6903 0.695 0.6086 0.2762 0.1435
No log 26.0 338 0.9872 0.695 0.4607 2.6336 0.695 0.5953 0.2728 0.1180
No log 27.0 351 0.9789 0.705 0.4580 2.6326 0.705 0.6127 0.2579 0.1171
No log 28.0 364 1.0033 0.685 0.4707 2.5747 0.685 0.5906 0.2747 0.1291
No log 29.0 377 1.0152 0.7 0.4789 2.4333 0.7 0.6260 0.2951 0.1739
No log 30.0 390 1.0107 0.715 0.4684 2.5194 0.715 0.6401 0.3197 0.1389
No log 31.0 403 0.9511 0.69 0.4445 2.5648 0.69 0.6131 0.2648 0.1298
No log 32.0 416 0.9586 0.735 0.4448 2.3342 0.735 0.6578 0.2941 0.1275
No log 33.0 429 1.0010 0.73 0.4625 2.4748 0.7300 0.6613 0.3307 0.1202
No log 34.0 442 0.9481 0.71 0.4361 2.4986 0.7100 0.6456 0.2856 0.1228
No log 35.0 455 0.9190 0.69 0.4323 2.6586 0.69 0.6265 0.2538 0.1250
No log 36.0 468 0.9226 0.715 0.4350 2.2652 0.715 0.6507 0.2868 0.1328
No log 37.0 481 0.9017 0.725 0.4182 2.5141 0.7250 0.6590 0.2547 0.1013
No log 38.0 494 0.9092 0.72 0.4218 2.5171 0.72 0.6495 0.2677 0.1055
1.0958 39.0 507 0.9093 0.71 0.4221 2.6479 0.7100 0.6456 0.2567 0.1185
1.0958 40.0 520 0.8926 0.71 0.4204 2.3785 0.7100 0.6522 0.2396 0.1153
1.0958 41.0 533 0.8928 0.715 0.4157 2.5719 0.715 0.6487 0.2708 0.1067
1.0958 42.0 546 0.8967 0.715 0.4247 2.6422 0.715 0.6495 0.2525 0.1174
1.0958 43.0 559 0.8773 0.695 0.4116 2.5548 0.695 0.6400 0.2491 0.1142
1.0958 44.0 572 0.8660 0.71 0.4036 2.2950 0.7100 0.6535 0.2401 0.1009
1.0958 45.0 585 0.8718 0.72 0.4057 2.4922 0.72 0.6551 0.2624 0.0998
1.0958 46.0 598 0.8737 0.7 0.4070 2.4455 0.7 0.6416 0.2360 0.1052
1.0958 47.0 611 0.8707 0.715 0.4094 2.3519 0.715 0.6494 0.2514 0.1086
1.0958 48.0 624 0.8640 0.705 0.4039 2.3765 0.705 0.6430 0.2538 0.1041
1.0958 49.0 637 0.8702 0.7 0.4066 2.5524 0.7 0.6423 0.2160 0.1080
1.0958 50.0 650 0.8672 0.71 0.4047 2.1924 0.7100 0.6463 0.2420 0.1050

Framework versions

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