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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_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_t2.5_a0.7
    results: []

resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_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: 0.8012
  • Accuracy: 0.7
  • Brier Loss: 0.4467
  • Nll: 2.5682
  • F1 Micro: 0.7
  • F1 Macro: 0.6313
  • Ece: 0.2684
  • Aurc: 0.1170

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 1.8024 0.16 0.8966 8.5001 0.16 0.1073 0.2079 0.8334
No log 2.0 26 1.7941 0.145 0.8957 8.3207 0.145 0.0843 0.2022 0.8435
No log 3.0 39 1.7486 0.2 0.8868 6.2015 0.2000 0.1007 0.2209 0.7900
No log 4.0 52 1.6854 0.205 0.8738 6.0142 0.205 0.0707 0.2453 0.7584
No log 5.0 65 1.6162 0.2 0.8594 6.2364 0.2000 0.0552 0.2466 0.7717
No log 6.0 78 1.5412 0.235 0.8416 6.0423 0.235 0.0902 0.2589 0.7006
No log 7.0 91 1.5011 0.295 0.8304 6.1420 0.295 0.1272 0.2803 0.6124
No log 8.0 104 1.4415 0.3 0.8114 6.0440 0.3 0.1296 0.2870 0.5641
No log 9.0 117 1.3257 0.38 0.7625 5.6923 0.38 0.2198 0.3136 0.3675
No log 10.0 130 1.3748 0.33 0.7905 5.5276 0.33 0.1870 0.2947 0.5985
No log 11.0 143 1.3294 0.39 0.7683 4.9632 0.39 0.2573 0.2940 0.4639
No log 12.0 156 1.2444 0.385 0.7297 4.8431 0.3850 0.2330 0.2849 0.4173
No log 13.0 169 1.2212 0.45 0.7153 4.5819 0.45 0.3051 0.3143 0.3379
No log 14.0 182 1.1835 0.495 0.6888 3.6108 0.495 0.3412 0.3316 0.2873
No log 15.0 195 1.1203 0.47 0.6559 3.6500 0.47 0.3348 0.2935 0.3061
No log 16.0 208 1.1520 0.495 0.6707 3.8106 0.495 0.3632 0.2938 0.3604
No log 17.0 221 1.0261 0.565 0.6021 3.3382 0.565 0.4214 0.2840 0.2047
No log 18.0 234 1.0080 0.61 0.5914 3.2936 0.61 0.4748 0.3240 0.1806
No log 19.0 247 1.0696 0.58 0.6253 3.2354 0.58 0.4686 0.3152 0.2626
No log 20.0 260 0.9733 0.615 0.5722 3.1019 0.615 0.4968 0.3259 0.2066
No log 21.0 273 0.9266 0.625 0.5423 3.0239 0.625 0.5202 0.2834 0.1782
No log 22.0 286 0.9364 0.66 0.5461 2.9031 0.66 0.5461 0.3128 0.1601
No log 23.0 299 0.9181 0.675 0.5307 2.8416 0.675 0.5584 0.3106 0.1462
No log 24.0 312 0.9739 0.665 0.5539 2.8798 0.665 0.5634 0.3325 0.1610
No log 25.0 325 0.8851 0.69 0.5099 2.7336 0.69 0.6013 0.3064 0.1437
No log 26.0 338 0.8755 0.71 0.4979 2.7400 0.7100 0.6032 0.3162 0.1211
No log 27.0 351 0.8653 0.675 0.4964 2.8339 0.675 0.5705 0.2977 0.1386
No log 28.0 364 0.8838 0.675 0.5055 2.7456 0.675 0.5816 0.2969 0.1524
No log 29.0 377 0.8805 0.68 0.5025 2.6942 0.68 0.5855 0.3099 0.1380
No log 30.0 390 0.8585 0.665 0.4891 2.7511 0.665 0.5737 0.2627 0.1370
No log 31.0 403 0.8410 0.675 0.4736 2.6431 0.675 0.5985 0.2670 0.1335
No log 32.0 416 0.8378 0.71 0.4724 2.7320 0.7100 0.6236 0.2885 0.1153
No log 33.0 429 0.8421 0.705 0.4718 2.6331 0.705 0.6326 0.2644 0.1147
No log 34.0 442 0.8350 0.685 0.4697 2.8035 0.685 0.6062 0.2831 0.1291
No log 35.0 455 0.8377 0.7 0.4708 2.4611 0.7 0.6376 0.3173 0.1195
No log 36.0 468 0.8126 0.69 0.4562 2.3909 0.69 0.6154 0.2433 0.1177
No log 37.0 481 0.8299 0.685 0.4673 2.5695 0.685 0.6080 0.2802 0.1261
No log 38.0 494 0.8197 0.685 0.4597 2.6388 0.685 0.6187 0.2690 0.1229
0.9314 39.0 507 0.8137 0.695 0.4547 2.7263 0.695 0.6332 0.2581 0.1207
0.9314 40.0 520 0.8168 0.69 0.4583 2.6230 0.69 0.6267 0.2696 0.1161
0.9314 41.0 533 0.8090 0.7 0.4529 2.6449 0.7 0.6236 0.2445 0.1187
0.9314 42.0 546 0.8168 0.68 0.4586 2.5516 0.68 0.6162 0.2722 0.1275
0.9314 43.0 559 0.8100 0.7 0.4523 2.5565 0.7 0.6347 0.2869 0.1192
0.9314 44.0 572 0.8078 0.7 0.4514 2.5734 0.7 0.6344 0.2583 0.1172
0.9314 45.0 585 0.8022 0.715 0.4472 2.4971 0.715 0.6534 0.2890 0.1165
0.9314 46.0 598 0.8049 0.695 0.4484 2.4891 0.695 0.6423 0.2722 0.1189
0.9314 47.0 611 0.8025 0.705 0.4481 2.4929 0.705 0.6393 0.2650 0.1124
0.9314 48.0 624 0.7973 0.7 0.4439 2.5000 0.7 0.6292 0.2718 0.1142
0.9314 49.0 637 0.8011 0.7 0.4464 2.5713 0.7 0.6303 0.2400 0.1183
0.9314 50.0 650 0.8012 0.7 0.4467 2.5682 0.7 0.6313 0.2684 0.1170

Framework versions

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