
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_a0.5

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: 0.6500
- Accuracy: 0.69
- Brier Loss: 0.5003
- Nll: 2.5629
- F1 Micro: 0.69
- F1 Macro: 0.6350
- Ece: 0.3098
- Aurc: 0.1329

## 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   | 1.4712          | 0.165    | 0.8966     | 8.4652 | 0.165    | 0.1101   | 0.2129 | 0.8342 |
| No log        | 2.0   | 26   | 1.4590          | 0.165    | 0.8951     | 8.1097 | 0.165    | 0.1059   | 0.2034 | 0.8021 |
| No log        | 3.0   | 39   | 1.4178          | 0.175    | 0.8873     | 6.8095 | 0.175    | 0.0813   | 0.2150 | 0.7994 |
| No log        | 4.0   | 52   | 1.3342          | 0.18     | 0.8702     | 6.4137 | 0.18     | 0.0475   | 0.2314 | 0.7558 |
| No log        | 5.0   | 65   | 1.2828          | 0.2      | 0.8587     | 6.1547 | 0.2000   | 0.0642   | 0.2429 | 0.7009 |
| No log        | 6.0   | 78   | 1.2675          | 0.205    | 0.8548     | 6.1395 | 0.205    | 0.0612   | 0.2348 | 0.7022 |
| No log        | 7.0   | 91   | 1.0716          | 0.31     | 0.7962     | 6.4589 | 0.31     | 0.1241   | 0.2787 | 0.4433 |
| No log        | 8.0   | 104  | 1.1184          | 0.29     | 0.8126     | 6.2585 | 0.29     | 0.1394   | 0.2863 | 0.5819 |
| No log        | 9.0   | 117  | 1.1021          | 0.31     | 0.8075     | 6.0370 | 0.31     | 0.1697   | 0.2834 | 0.5458 |
| No log        | 10.0  | 130  | 1.0268          | 0.33     | 0.7815     | 6.1370 | 0.33     | 0.1921   | 0.2856 | 0.5395 |
| No log        | 11.0  | 143  | 1.0290          | 0.355    | 0.7759     | 5.3640 | 0.3550   | 0.2143   | 0.2795 | 0.4697 |
| No log        | 12.0  | 156  | 0.9169          | 0.36     | 0.7262     | 5.2997 | 0.36     | 0.1995   | 0.2761 | 0.4070 |
| No log        | 13.0  | 169  | 0.9903          | 0.36     | 0.7586     | 4.9404 | 0.36     | 0.2200   | 0.2832 | 0.5343 |
| No log        | 14.0  | 182  | 0.9128          | 0.425    | 0.7082     | 4.5862 | 0.425    | 0.2706   | 0.2834 | 0.3542 |
| No log        | 15.0  | 195  | 1.0046          | 0.405    | 0.7441     | 3.9763 | 0.405    | 0.2759   | 0.3142 | 0.4602 |
| No log        | 16.0  | 208  | 0.9277          | 0.41     | 0.7146     | 4.3670 | 0.41     | 0.2763   | 0.2695 | 0.4409 |
| No log        | 17.0  | 221  | 0.9726          | 0.505    | 0.7208     | 3.5350 | 0.505    | 0.3736   | 0.3332 | 0.3469 |
| No log        | 18.0  | 234  | 0.7717          | 0.505    | 0.6280     | 3.4386 | 0.505    | 0.3412   | 0.2564 | 0.2567 |
| No log        | 19.0  | 247  | 0.7723          | 0.58     | 0.6143     | 3.6207 | 0.58     | 0.4125   | 0.3178 | 0.1847 |
| No log        | 20.0  | 260  | 0.8182          | 0.57     | 0.6419     | 3.1633 | 0.57     | 0.4855   | 0.3517 | 0.2530 |
| No log        | 21.0  | 273  | 0.7333          | 0.58     | 0.5891     | 3.3014 | 0.58     | 0.4512   | 0.2718 | 0.2137 |
| No log        | 22.0  | 286  | 0.7374          | 0.665    | 0.5856     | 3.0299 | 0.665    | 0.5432   | 0.3459 | 0.1657 |
| No log        | 23.0  | 299  | 0.7083          | 0.645    | 0.5564     | 3.0874 | 0.645    | 0.5180   | 0.3112 | 0.1608 |
| No log        | 24.0  | 312  | 0.7480          | 0.64     | 0.5901     | 3.0218 | 0.64     | 0.5410   | 0.3701 | 0.1976 |
| No log        | 25.0  | 325  | 0.7547          | 0.68     | 0.5894     | 2.9002 | 0.68     | 0.5801   | 0.3817 | 0.1559 |
| No log        | 26.0  | 338  | 0.6998          | 0.65     | 0.5474     | 2.9402 | 0.65     | 0.5468   | 0.2875 | 0.1707 |
| No log        | 27.0  | 351  | 0.6967          | 0.66     | 0.5506     | 2.8344 | 0.66     | 0.5578   | 0.3105 | 0.1707 |
| No log        | 28.0  | 364  | 0.6733          | 0.655    | 0.5332     | 2.6492 | 0.655    | 0.5719   | 0.2935 | 0.1554 |
| No log        | 29.0  | 377  | 0.7162          | 0.67     | 0.5596     | 2.7250 | 0.67     | 0.5721   | 0.3388 | 0.1423 |
| No log        | 30.0  | 390  | 0.6826          | 0.665    | 0.5291     | 2.7460 | 0.665    | 0.5797   | 0.3353 | 0.1469 |
| No log        | 31.0  | 403  | 0.6761          | 0.665    | 0.5195     | 2.7938 | 0.665    | 0.5647   | 0.3096 | 0.1485 |
| No log        | 32.0  | 416  | 0.6745          | 0.695    | 0.5295     | 2.6172 | 0.695    | 0.6160   | 0.3171 | 0.1636 |
| No log        | 33.0  | 429  | 0.6785          | 0.695    | 0.5242     | 2.5816 | 0.695    | 0.6115   | 0.3475 | 0.1349 |
| No log        | 34.0  | 442  | 0.6688          | 0.665    | 0.5174     | 2.6401 | 0.665    | 0.5833   | 0.2988 | 0.1427 |
| No log        | 35.0  | 455  | 0.6767          | 0.675    | 0.5275     | 2.6364 | 0.675    | 0.6027   | 0.3285 | 0.1483 |
| No log        | 36.0  | 468  | 0.6605          | 0.695    | 0.5076     | 2.6483 | 0.695    | 0.6252   | 0.3127 | 0.1372 |
| No log        | 37.0  | 481  | 0.6538          | 0.705    | 0.5029     | 2.6284 | 0.705    | 0.6340   | 0.3173 | 0.1220 |
| No log        | 38.0  | 494  | 0.6610          | 0.695    | 0.5102     | 2.5052 | 0.695    | 0.6375   | 0.3128 | 0.1298 |
| 0.7532        | 39.0  | 507  | 0.6618          | 0.695    | 0.5110     | 2.5663 | 0.695    | 0.6268   | 0.3297 | 0.1367 |
| 0.7532        | 40.0  | 520  | 0.6749          | 0.69     | 0.5235     | 2.5343 | 0.69     | 0.6341   | 0.3256 | 0.1332 |
| 0.7532        | 41.0  | 533  | 0.6574          | 0.695    | 0.5062     | 2.4223 | 0.695    | 0.6338   | 0.3292 | 0.1469 |
| 0.7532        | 42.0  | 546  | 0.6530          | 0.695    | 0.5026     | 2.6189 | 0.695    | 0.6390   | 0.2950 | 0.1391 |
| 0.7532        | 43.0  | 559  | 0.6509          | 0.685    | 0.5003     | 2.5417 | 0.685    | 0.6299   | 0.3150 | 0.1368 |
| 0.7532        | 44.0  | 572  | 0.6520          | 0.71     | 0.5030     | 2.4796 | 0.7100   | 0.6453   | 0.3251 | 0.1286 |
| 0.7532        | 45.0  | 585  | 0.6494          | 0.69     | 0.4994     | 2.5431 | 0.69     | 0.6327   | 0.3138 | 0.1279 |
| 0.7532        | 46.0  | 598  | 0.6515          | 0.71     | 0.5007     | 2.5295 | 0.7100   | 0.6541   | 0.3307 | 0.1208 |
| 0.7532        | 47.0  | 611  | 0.6477          | 0.69     | 0.4979     | 2.5971 | 0.69     | 0.6323   | 0.3263 | 0.1281 |
| 0.7532        | 48.0  | 624  | 0.6495          | 0.7      | 0.5007     | 2.6162 | 0.7      | 0.6395   | 0.3412 | 0.1272 |
| 0.7532        | 49.0  | 637  | 0.6478          | 0.7      | 0.4968     | 2.4946 | 0.7      | 0.6386   | 0.3191 | 0.1309 |
| 0.7532        | 50.0  | 650  | 0.6500          | 0.69     | 0.5003     | 2.5629 | 0.69     | 0.6350   | 0.3098 | 0.1329 |


### 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_t1.5_a0.5", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us