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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9

Image Classification
Transformers
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_t5.0_a0.9
    results: []

resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_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: 0.8809
  • Accuracy: 0.7
  • Brier Loss: 0.4126
  • Nll: 2.4279
  • F1 Micro: 0.7
  • F1 Macro: 0.6279
  • Ece: 0.2569
  • Aurc: 0.1111

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 2.1185 0.165 0.8967 8.5399 0.165 0.1130 0.2151 0.8331
No log 2.0 26 2.1127 0.13 0.8958 8.1152 0.13 0.0842 0.1816 0.8392
No log 3.0 39 2.0781 0.165 0.8888 6.8828 0.165 0.0878 0.2150 0.8082
No log 4.0 52 2.0197 0.22 0.8762 5.7578 0.22 0.1155 0.2521 0.7521
No log 5.0 65 1.9499 0.205 0.8601 6.0641 0.205 0.0951 0.2567 0.7355
No log 6.0 78 1.9019 0.25 0.8483 5.8930 0.25 0.1178 0.2728 0.6862
No log 7.0 91 1.8252 0.28 0.8301 5.8062 0.28 0.1660 0.2890 0.6982
No log 8.0 104 1.8194 0.28 0.8275 5.2642 0.28 0.1625 0.2874 0.6935
No log 9.0 117 1.7671 0.355 0.8109 5.1326 0.3550 0.2211 0.3018 0.5678
No log 10.0 130 1.6582 0.355 0.7774 5.2226 0.3550 0.2200 0.2991 0.5305
No log 11.0 143 1.5849 0.395 0.7422 5.0239 0.395 0.2436 0.2979 0.3974
No log 12.0 156 1.4908 0.46 0.7001 4.2790 0.46 0.3169 0.3091 0.3003
No log 13.0 169 1.6016 0.395 0.7496 4.2149 0.395 0.2793 0.2929 0.4640
No log 14.0 182 1.4714 0.475 0.6971 4.0742 0.4750 0.3299 0.3177 0.3613
No log 15.0 195 1.5007 0.46 0.7119 3.8252 0.46 0.3145 0.3111 0.3954
No log 16.0 208 1.4352 0.515 0.6776 3.4028 0.515 0.3948 0.3376 0.2993
No log 17.0 221 1.2890 0.575 0.6104 3.4453 0.575 0.4478 0.2940 0.2119
No log 18.0 234 1.2190 0.595 0.5719 3.2413 0.595 0.4662 0.2608 0.1981
No log 19.0 247 1.2287 0.59 0.5764 3.2303 0.59 0.4857 0.2811 0.2020
No log 20.0 260 1.1726 0.64 0.5494 2.9544 0.64 0.5307 0.2993 0.1708
No log 21.0 273 1.1305 0.61 0.5384 2.9557 0.61 0.5170 0.2771 0.1949
No log 22.0 286 1.1256 0.645 0.5295 2.7934 0.645 0.5381 0.3181 0.1629
No log 23.0 299 1.1209 0.645 0.5217 2.8697 0.645 0.5432 0.3055 0.1687
No log 24.0 312 1.2513 0.685 0.5917 2.7262 0.685 0.5639 0.3779 0.1833
No log 25.0 325 1.0321 0.695 0.4819 2.7202 0.695 0.5896 0.2810 0.1280
No log 26.0 338 1.0405 0.645 0.4957 2.6116 0.645 0.5661 0.2515 0.1700
No log 27.0 351 1.0580 0.695 0.4933 2.7436 0.695 0.5996 0.2967 0.1339
No log 28.0 364 0.9740 0.65 0.4575 2.5682 0.65 0.5731 0.2513 0.1384
No log 29.0 377 0.9934 0.695 0.4651 2.5753 0.695 0.6108 0.2775 0.1171
No log 30.0 390 0.9900 0.645 0.4695 2.6280 0.645 0.5668 0.2459 0.1558
No log 31.0 403 0.9671 0.695 0.4504 2.8174 0.695 0.6094 0.2505 0.1188
No log 32.0 416 0.9327 0.715 0.4324 2.5285 0.715 0.6415 0.2565 0.1086
No log 33.0 429 0.9628 0.71 0.4464 2.5876 0.7100 0.6435 0.2709 0.1152
No log 34.0 442 0.9316 0.715 0.4353 2.7111 0.715 0.6334 0.2361 0.1078
No log 35.0 455 0.9275 0.7 0.4364 2.5226 0.7 0.6251 0.2586 0.1207
No log 36.0 468 0.9301 0.7 0.4346 2.6464 0.7 0.6232 0.2482 0.1142
No log 37.0 481 0.9013 0.695 0.4194 2.5575 0.695 0.6197 0.2554 0.1098
No log 38.0 494 0.9008 0.695 0.4196 2.6270 0.695 0.6156 0.2246 0.1063
1.0903 39.0 507 0.9185 0.71 0.4311 2.6290 0.7100 0.6362 0.2626 0.1165
1.0903 40.0 520 0.9053 0.685 0.4254 2.5057 0.685 0.6239 0.2210 0.1171
1.0903 41.0 533 0.8955 0.7 0.4189 2.4823 0.7 0.6291 0.1995 0.1103
1.0903 42.0 546 0.9012 0.69 0.4223 2.5377 0.69 0.6195 0.2486 0.1119
1.0903 43.0 559 0.8894 0.71 0.4138 2.6167 0.7100 0.6382 0.2459 0.1022
1.0903 44.0 572 0.8846 0.695 0.4132 2.5130 0.695 0.6265 0.2198 0.1093
1.0903 45.0 585 0.8946 0.69 0.4190 2.6357 0.69 0.6230 0.2375 0.1145
1.0903 46.0 598 0.8931 0.705 0.4168 2.6306 0.705 0.6342 0.2555 0.1102
1.0903 47.0 611 0.8842 0.71 0.4160 2.3021 0.7100 0.6347 0.2096 0.1120
1.0903 48.0 624 0.8805 0.695 0.4140 2.3447 0.695 0.6237 0.2181 0.1128
1.0903 49.0 637 0.8816 0.7 0.4142 2.4358 0.7 0.6295 0.2550 0.1112
1.0903 50.0 650 0.8809 0.7 0.4126 2.4279 0.7 0.6279 0.2569 0.1111

Framework versions

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