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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7

This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/resnet-50", "model-index": [{"name": "resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us