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# resnet101-base_tobacco-cnn_tobacco3482_simkd

This model is a fine-tuned version of [bdpc/resnet101-base_tobacco](https://huggingface.co/bdpc/resnet101-base_tobacco) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 13.1229
- Accuracy: 0.295
- Brier Loss: 0.7636
- Nll: 6.8757
- F1 Micro: 0.295
- F1 Macro: 0.1150
- Ece: 0.2446
- Aurc: 0.4919

## 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: 128
- eval_batch_size: 128
- 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   | 7    | 0.2512          | 0.18     | 0.9617     | 7.0686 | 0.18     | 0.0305   | 0.3439 | 0.7810 |
| No log        | 2.0   | 14   | 0.3629          | 0.18     | 1.0943     | 7.0153 | 0.18     | 0.0305   | 0.4345 | 0.8186 |
| No log        | 3.0   | 21   | 0.4745          | 0.18     | 1.1577     | 6.9805 | 0.18     | 0.0305   | 0.5034 | 0.8029 |
| No log        | 4.0   | 28   | 0.6953          | 0.18     | 1.1290     | 6.9352 | 0.18     | 0.0305   | 0.4731 | 0.8367 |
| No log        | 5.0   | 35   | 173.4450        | 0.18     | 1.1346     | 6.8314 | 0.18     | 0.0305   | 0.4615 | 0.8814 |
| No log        | 6.0   | 42   | 412.7549        | 0.18     | 1.1098     | 6.8364 | 0.18     | 0.0305   | 0.4420 | 0.8716 |
| No log        | 7.0   | 49   | 148.0839        | 0.18     | 1.0291     | 6.9271 | 0.18     | 0.0305   | 0.3960 | 0.7698 |
| No log        | 8.0   | 56   | 61.2696         | 0.18     | 0.9674     | 6.9593 | 0.18     | 0.0305   | 0.3413 | 0.7924 |
| No log        | 9.0   | 63   | 175.4512        | 0.18     | 0.9708     | 6.9854 | 0.18     | 0.0305   | 0.3549 | 0.8252 |
| No log        | 10.0  | 70   | 139.2036        | 0.18     | 0.9400     | 6.9022 | 0.18     | 0.0305   | 0.3300 | 0.7760 |
| No log        | 11.0  | 77   | 12.5605         | 0.295    | 0.8656     | 6.9766 | 0.295    | 0.1138   | 0.3093 | 0.5354 |
| No log        | 12.0  | 84   | 2.3147          | 0.18     | 0.9363     | 6.9778 | 0.18     | 0.0305   | 0.3084 | 0.7507 |
| No log        | 13.0  | 91   | 75.2050         | 0.18     | 0.9543     | 9.1566 | 0.18     | 0.0305   | 0.2990 | 0.7716 |
| No log        | 14.0  | 98   | 37.4873         | 0.18     | 0.9410     | 9.1473 | 0.18     | 0.0305   | 0.3029 | 0.7517 |
| No log        | 15.0  | 105  | 8.5750          | 0.18     | 0.9304     | 9.1440 | 0.18     | 0.0305   | 0.3033 | 0.7718 |
| No log        | 16.0  | 112  | 21.5310         | 0.18     | 0.9232     | 9.1349 | 0.18     | 0.0305   | 0.3122 | 0.7717 |
| No log        | 17.0  | 119  | 66.9546         | 0.18     | 0.9287     | 9.1376 | 0.18     | 0.0305   | 0.2920 | 0.7715 |
| No log        | 18.0  | 126  | 2.6525          | 0.285    | 0.8357     | 7.0773 | 0.285    | 0.1143   | 0.3156 | 0.5306 |
| No log        | 19.0  | 133  | 7.7253          | 0.24     | 0.8574     | 7.0190 | 0.24     | 0.0880   | 0.2948 | 0.7186 |
| No log        | 20.0  | 140  | 30.0305         | 0.285    | 0.8086     | 6.9862 | 0.285    | 0.1133   | 0.3001 | 0.5273 |
| No log        | 21.0  | 147  | 3.9243          | 0.18     | 0.8680     | 7.4799 | 0.18     | 0.0306   | 0.2739 | 0.7704 |
| No log        | 22.0  | 154  | 4.4660          | 0.18     | 0.8831     | 8.9935 | 0.18     | 0.0308   | 0.2652 | 0.7313 |
| No log        | 23.0  | 161  | 3.9728          | 0.18     | 0.8719     | 8.9609 | 0.18     | 0.0308   | 0.2600 | 0.7651 |
| No log        | 24.0  | 168  | 2.6913          | 0.285    | 0.8089     | 6.9969 | 0.285    | 0.1146   | 0.2873 | 0.5122 |
| No log        | 25.0  | 175  | 1.3141          | 0.29     | 0.8086     | 7.0227 | 0.29     | 0.1156   | 0.3154 | 0.5256 |
| No log        | 26.0  | 182  | 13.5853         | 0.29     | 0.7782     | 6.8763 | 0.29     | 0.1168   | 0.2735 | 0.5045 |
| No log        | 27.0  | 189  | 11.9763         | 0.3      | 0.7730     | 6.8499 | 0.3      | 0.1171   | 0.2740 | 0.4971 |
| No log        | 28.0  | 196  | 1.6467          | 0.285    | 0.8067     | 7.1641 | 0.285    | 0.1144   | 0.2870 | 0.5193 |
| No log        | 29.0  | 203  | 30.5306         | 0.285    | 0.8424     | 7.1576 | 0.285    | 0.1129   | 0.2686 | 0.6662 |
| No log        | 30.0  | 210  | 13.5964         | 0.18     | 0.8584     | 7.0972 | 0.18     | 0.0305   | 0.2704 | 0.7307 |
| No log        | 31.0  | 217  | 98.3061         | 0.29     | 0.8274     | 7.0330 | 0.29     | 0.1167   | 0.3163 | 0.5653 |
| No log        | 32.0  | 224  | 53.0911         | 0.29     | 0.7984     | 6.9311 | 0.29     | 0.1167   | 0.2911 | 0.5181 |
| No log        | 33.0  | 231  | 2.2010          | 0.265    | 0.8291     | 6.9883 | 0.265    | 0.1037   | 0.2945 | 0.6039 |
| No log        | 34.0  | 238  | 3.6255          | 0.295    | 0.7836     | 6.8954 | 0.295    | 0.1176   | 0.2636 | 0.5025 |
| No log        | 35.0  | 245  | 0.9640          | 0.3      | 0.7571     | 6.7913 | 0.3      | 0.1170   | 0.2388 | 0.4746 |
| No log        | 36.0  | 252  | 1.1935          | 0.295    | 0.7711     | 6.7993 | 0.295    | 0.1175   | 0.2619 | 0.4779 |
| No log        | 37.0  | 259  | 12.7465         | 0.305    | 0.7650     | 6.8142 | 0.305    | 0.1205   | 0.2512 | 0.4798 |
| No log        | 38.0  | 266  | 56.6876         | 0.305    | 0.7840     | 6.8750 | 0.305    | 0.1205   | 0.2835 | 0.4985 |
| No log        | 39.0  | 273  | 122.6602        | 0.295    | 0.7919     | 6.9220 | 0.295    | 0.1116   | 0.2493 | 0.5312 |
| No log        | 40.0  | 280  | 14.4685         | 0.295    | 0.7757     | 6.8232 | 0.295    | 0.1162   | 0.2575 | 0.4988 |
| No log        | 41.0  | 287  | 3.9605          | 0.295    | 0.7601     | 6.7809 | 0.295    | 0.1138   | 0.2437 | 0.4911 |
| No log        | 42.0  | 294  | 7.9424          | 0.295    | 0.7567     | 6.7609 | 0.295    | 0.1138   | 0.2398 | 0.4883 |
| No log        | 43.0  | 301  | 17.7810         | 0.295    | 0.7713     | 6.8075 | 0.295    | 0.1175   | 0.2503 | 0.5090 |
| No log        | 44.0  | 308  | 30.8773         | 0.295    | 0.7747     | 6.8248 | 0.295    | 0.1127   | 0.2651 | 0.5149 |
| No log        | 45.0  | 315  | 16.3877         | 0.29     | 0.7736     | 6.8888 | 0.29     | 0.1117   | 0.2641 | 0.5026 |
| No log        | 46.0  | 322  | 7.4195          | 0.29     | 0.7674     | 6.8179 | 0.29     | 0.1117   | 0.2621 | 0.4991 |
| No log        | 47.0  | 329  | 9.6560          | 0.295    | 0.7694     | 6.8960 | 0.295    | 0.1138   | 0.2604 | 0.4963 |
| No log        | 48.0  | 336  | 6.6040          | 0.29     | 0.7622     | 6.7835 | 0.29     | 0.1117   | 0.2271 | 0.4958 |
| No log        | 49.0  | 343  | 10.3365         | 0.29     | 0.7640     | 6.8293 | 0.29     | 0.1117   | 0.2583 | 0.4941 |
| No log        | 50.0  | 350  | 13.1229         | 0.295    | 0.7636     | 6.8757 | 0.295    | 0.1150   | 0.2446 | 0.4919 |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.2.0.dev20231112+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1

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