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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7

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_a0.7
    results: []

resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_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: 0.7861
  • Accuracy: 0.705
  • Brier Loss: 0.4410
  • Nll: 2.6519
  • F1 Micro: 0.705
  • F1 Macro: 0.6403
  • Ece: 0.2724
  • Aurc: 0.1188

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 1.7831 0.165 0.8966 8.4414 0.165 0.1121 0.2151 0.8335
No log 2.0 26 1.7753 0.145 0.8958 8.5715 0.145 0.0954 0.1998 0.8332
No log 3.0 39 1.7334 0.175 0.8877 6.4682 0.175 0.0756 0.2069 0.7896
No log 4.0 52 1.6604 0.185 0.8723 6.0351 0.185 0.0505 0.2328 0.7549
No log 5.0 65 1.5874 0.18 0.8560 6.0732 0.18 0.0431 0.2285 0.7506
No log 6.0 78 1.5223 0.185 0.8415 6.1638 0.185 0.0479 0.2419 0.7530
No log 7.0 91 1.4642 0.35 0.8239 6.0328 0.35 0.1696 0.3081 0.5219
No log 8.0 104 1.3599 0.35 0.7825 6.2102 0.35 0.1908 0.2977 0.4172
No log 9.0 117 1.3083 0.385 0.7566 5.7128 0.3850 0.2203 0.3012 0.3842
No log 10.0 130 1.3151 0.365 0.7670 5.1073 0.3650 0.2150 0.2923 0.4891
No log 11.0 143 1.3736 0.295 0.7950 5.3584 0.295 0.1747 0.2716 0.6360
No log 12.0 156 1.2655 0.425 0.7380 4.0312 0.425 0.2789 0.3273 0.3366
No log 13.0 169 1.1696 0.475 0.6901 3.9627 0.4750 0.3083 0.3011 0.2825
No log 14.0 182 1.2992 0.355 0.7473 3.9098 0.3550 0.2292 0.2675 0.4929
No log 15.0 195 1.1698 0.51 0.6881 3.7143 0.51 0.3691 0.3333 0.3278
No log 16.0 208 1.0624 0.515 0.6274 3.8387 0.515 0.3631 0.2821 0.2583
No log 17.0 221 1.0970 0.565 0.6421 3.3302 0.565 0.4493 0.3362 0.2373
No log 18.0 234 1.0029 0.625 0.5883 3.3820 0.625 0.4675 0.3005 0.1660
No log 19.0 247 1.0384 0.605 0.6093 3.3183 0.605 0.4863 0.3252 0.2145
No log 20.0 260 1.0686 0.62 0.6234 3.0246 0.62 0.5155 0.3625 0.2334
No log 21.0 273 0.9641 0.62 0.5685 2.9225 0.62 0.5259 0.3103 0.2063
No log 22.0 286 1.0054 0.665 0.5849 3.0792 0.665 0.5614 0.3636 0.1863
No log 23.0 299 0.9959 0.675 0.5734 2.9829 0.675 0.5577 0.3619 0.1806
No log 24.0 312 0.9044 0.675 0.5267 2.8952 0.675 0.5712 0.2989 0.1475
No log 25.0 325 0.9803 0.655 0.5627 2.7501 0.655 0.5418 0.3415 0.1919
No log 26.0 338 0.8814 0.65 0.5176 2.8421 0.65 0.5619 0.2665 0.1694
No log 27.0 351 0.8555 0.69 0.4928 2.7870 0.69 0.5831 0.3091 0.1279
No log 28.0 364 0.8290 0.69 0.4777 2.6377 0.69 0.5976 0.2551 0.1290
No log 29.0 377 0.8593 0.685 0.4949 2.5880 0.685 0.5776 0.3083 0.1279
No log 30.0 390 0.8226 0.685 0.4678 2.8938 0.685 0.5884 0.2820 0.1249
No log 31.0 403 0.8578 0.69 0.4857 2.6150 0.69 0.6024 0.3109 0.1344
No log 32.0 416 0.8330 0.685 0.4753 2.5999 0.685 0.6047 0.2688 0.1407
No log 33.0 429 0.8268 0.7 0.4683 2.6138 0.7 0.6193 0.2913 0.1315
No log 34.0 442 0.8535 0.715 0.4749 2.5059 0.715 0.6450 0.2931 0.1190
No log 35.0 455 0.8334 0.665 0.4752 2.3839 0.665 0.5950 0.2762 0.1397
No log 36.0 468 0.8025 0.71 0.4553 2.4803 0.7100 0.6302 0.2889 0.1178
No log 37.0 481 0.8142 0.715 0.4563 2.6785 0.715 0.6426 0.2989 0.1048
No log 38.0 494 0.8124 0.7 0.4538 2.5320 0.7 0.6332 0.2594 0.1132
0.9303 39.0 507 0.7888 0.69 0.4452 2.6427 0.69 0.6269 0.2583 0.1224
0.9303 40.0 520 0.7907 0.705 0.4458 2.6942 0.705 0.6367 0.2688 0.1155
0.9303 41.0 533 0.7918 0.71 0.4442 2.4378 0.7100 0.6558 0.2816 0.1132
0.9303 42.0 546 0.8005 0.725 0.4479 2.6088 0.7250 0.6576 0.2914 0.1049
0.9303 43.0 559 0.7879 0.72 0.4421 2.7052 0.72 0.6592 0.2741 0.1122
0.9303 44.0 572 0.7910 0.71 0.4461 2.6463 0.7100 0.6463 0.3119 0.1188
0.9303 45.0 585 0.7922 0.705 0.4450 2.6453 0.705 0.6481 0.2753 0.1211
0.9303 46.0 598 0.7915 0.715 0.4429 2.6970 0.715 0.6526 0.2741 0.1107
0.9303 47.0 611 0.7809 0.705 0.4370 2.6841 0.705 0.6453 0.2734 0.1158
0.9303 48.0 624 0.7771 0.705 0.4350 2.6168 0.705 0.6423 0.2652 0.1139
0.9303 49.0 637 0.7826 0.705 0.4377 2.5091 0.705 0.6423 0.2758 0.1202
0.9303 50.0 650 0.7861 0.705 0.4410 2.6519 0.705 0.6403 0.2724 0.1188

Framework versions

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