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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_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: 0.6481
- Accuracy: 0.69
- Brier Loss: 0.4919
- Nll: 2.4969
- F1 Micro: 0.69
- F1 Macro: 0.6317
- Ece: 0.3029
- Aurc: 0.1260

## 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   | 1.4796          | 0.165    | 0.8965     | 8.4885 | 0.165    | 0.1123   | 0.2151 | 0.8341 |
| No log        | 2.0   | 26   | 1.4679          | 0.165    | 0.8954     | 8.3391 | 0.165    | 0.1066   | 0.2136 | 0.8332 |
| No log        | 3.0   | 39   | 1.4170          | 0.21     | 0.8858     | 6.1941 | 0.2100   | 0.0969   | 0.2433 | 0.7991 |
| No log        | 4.0   | 52   | 1.3472          | 0.21     | 0.8711     | 6.0602 | 0.2100   | 0.0728   | 0.2320 | 0.7271 |
| No log        | 5.0   | 65   | 1.2776          | 0.19     | 0.8572     | 6.1293 | 0.19     | 0.0537   | 0.2422 | 0.7473 |
| No log        | 6.0   | 78   | 1.1840          | 0.245    | 0.8353     | 6.2405 | 0.245    | 0.1060   | 0.2810 | 0.6690 |
| No log        | 7.0   | 91   | 1.0740          | 0.365    | 0.7936     | 6.3617 | 0.3650   | 0.1739   | 0.3136 | 0.3646 |
| No log        | 8.0   | 104  | 1.1102          | 0.345    | 0.8081     | 5.8896 | 0.345    | 0.1812   | 0.3046 | 0.4292 |
| No log        | 9.0   | 117  | 1.0735          | 0.34     | 0.7963     | 5.9970 | 0.34     | 0.1842   | 0.3028 | 0.4286 |
| No log        | 10.0  | 130  | 1.1145          | 0.265    | 0.8110     | 5.9054 | 0.265    | 0.1300   | 0.2511 | 0.6350 |
| No log        | 11.0  | 143  | 0.9981          | 0.325    | 0.7659     | 5.3834 | 0.325    | 0.1655   | 0.2790 | 0.4860 |
| No log        | 12.0  | 156  | 1.0500          | 0.285    | 0.7898     | 4.9696 | 0.285    | 0.1594   | 0.2604 | 0.6636 |
| No log        | 13.0  | 169  | 0.8764          | 0.445    | 0.6976     | 4.6456 | 0.445    | 0.2647   | 0.2779 | 0.3020 |
| No log        | 14.0  | 182  | 0.9147          | 0.48     | 0.7108     | 4.4793 | 0.48     | 0.2942   | 0.3262 | 0.2862 |
| No log        | 15.0  | 195  | 0.9776          | 0.38     | 0.7434     | 4.4065 | 0.38     | 0.2269   | 0.2938 | 0.5297 |
| No log        | 16.0  | 208  | 0.8066          | 0.47     | 0.6494     | 3.9671 | 0.47     | 0.2966   | 0.2791 | 0.2907 |
| No log        | 17.0  | 221  | 0.7766          | 0.535    | 0.6305     | 3.5250 | 0.535    | 0.3866   | 0.3003 | 0.2424 |
| No log        | 18.0  | 234  | 0.8186          | 0.535    | 0.6458     | 3.3670 | 0.535    | 0.3792   | 0.3005 | 0.2311 |
| No log        | 19.0  | 247  | 0.8156          | 0.52     | 0.6430     | 3.1633 | 0.52     | 0.3675   | 0.3072 | 0.2667 |
| No log        | 20.0  | 260  | 0.8386          | 0.55     | 0.6462     | 3.2549 | 0.55     | 0.4251   | 0.3103 | 0.2703 |
| No log        | 21.0  | 273  | 0.7996          | 0.515    | 0.6342     | 3.1396 | 0.515    | 0.3969   | 0.3177 | 0.2867 |
| No log        | 22.0  | 286  | 0.8605          | 0.6      | 0.6472     | 3.2563 | 0.6      | 0.4717   | 0.3810 | 0.2113 |
| No log        | 23.0  | 299  | 0.7138          | 0.595    | 0.5713     | 3.1171 | 0.595    | 0.4657   | 0.2773 | 0.2034 |
| No log        | 24.0  | 312  | 0.7212          | 0.665    | 0.5740     | 2.9688 | 0.665    | 0.5474   | 0.3366 | 0.1754 |
| No log        | 25.0  | 325  | 0.7463          | 0.63     | 0.5843     | 2.8998 | 0.63     | 0.5502   | 0.3432 | 0.2072 |
| No log        | 26.0  | 338  | 0.7231          | 0.67     | 0.5626     | 3.1334 | 0.67     | 0.5564   | 0.3160 | 0.1521 |
| No log        | 27.0  | 351  | 0.6913          | 0.68     | 0.5427     | 2.8906 | 0.68     | 0.5702   | 0.3354 | 0.1406 |
| No log        | 28.0  | 364  | 0.6825          | 0.66     | 0.5342     | 2.8619 | 0.66     | 0.5615   | 0.2902 | 0.1625 |
| No log        | 29.0  | 377  | 0.7015          | 0.665    | 0.5549     | 2.7315 | 0.665    | 0.5741   | 0.3305 | 0.1769 |
| No log        | 30.0  | 390  | 0.6939          | 0.67     | 0.5406     | 2.7114 | 0.67     | 0.5720   | 0.3353 | 0.1420 |
| No log        | 31.0  | 403  | 0.6836          | 0.69     | 0.5265     | 2.7567 | 0.69     | 0.5982   | 0.3216 | 0.1455 |
| No log        | 32.0  | 416  | 0.6728          | 0.69     | 0.5211     | 2.6858 | 0.69     | 0.6056   | 0.3124 | 0.1453 |
| No log        | 33.0  | 429  | 0.6926          | 0.675    | 0.5403     | 2.5815 | 0.675    | 0.6095   | 0.3258 | 0.1683 |
| No log        | 34.0  | 442  | 0.6673          | 0.66     | 0.5090     | 2.5591 | 0.66     | 0.5722   | 0.2950 | 0.1385 |
| No log        | 35.0  | 455  | 0.6811          | 0.675    | 0.5207     | 2.5813 | 0.675    | 0.5841   | 0.3324 | 0.1273 |
| No log        | 36.0  | 468  | 0.6648          | 0.69     | 0.5119     | 2.5745 | 0.69     | 0.6225   | 0.3433 | 0.1320 |
| No log        | 37.0  | 481  | 0.6623          | 0.67     | 0.5092     | 2.6134 | 0.67     | 0.6129   | 0.3204 | 0.1471 |
| No log        | 38.0  | 494  | 0.6635          | 0.69     | 0.5088     | 2.3862 | 0.69     | 0.6192   | 0.3201 | 0.1311 |
| 0.7628        | 39.0  | 507  | 0.6554          | 0.685    | 0.5008     | 2.5849 | 0.685    | 0.6210   | 0.3179 | 0.1377 |
| 0.7628        | 40.0  | 520  | 0.6567          | 0.685    | 0.5022     | 2.6498 | 0.685    | 0.6310   | 0.3127 | 0.1414 |
| 0.7628        | 41.0  | 533  | 0.6558          | 0.695    | 0.4996     | 2.5917 | 0.695    | 0.6347   | 0.3115 | 0.1321 |
| 0.7628        | 42.0  | 546  | 0.6578          | 0.695    | 0.5021     | 2.4864 | 0.695    | 0.6259   | 0.3098 | 0.1306 |
| 0.7628        | 43.0  | 559  | 0.6544          | 0.685    | 0.4969     | 2.5757 | 0.685    | 0.6175   | 0.2955 | 0.1342 |
| 0.7628        | 44.0  | 572  | 0.6507          | 0.685    | 0.4944     | 2.5057 | 0.685    | 0.6257   | 0.3144 | 0.1304 |
| 0.7628        | 45.0  | 585  | 0.6501          | 0.675    | 0.4937     | 2.4903 | 0.675    | 0.6208   | 0.3091 | 0.1301 |
| 0.7628        | 46.0  | 598  | 0.6518          | 0.685    | 0.4949     | 2.4732 | 0.685    | 0.6254   | 0.3164 | 0.1235 |
| 0.7628        | 47.0  | 611  | 0.6499          | 0.685    | 0.4936     | 2.4924 | 0.685    | 0.6273   | 0.3124 | 0.1323 |
| 0.7628        | 48.0  | 624  | 0.6490          | 0.7      | 0.4925     | 2.4999 | 0.7      | 0.6353   | 0.3147 | 0.1243 |
| 0.7628        | 49.0  | 637  | 0.6510          | 0.685    | 0.4933     | 2.5758 | 0.685    | 0.6242   | 0.3206 | 0.1281 |
| 0.7628        | 50.0  | 650  | 0.6481          | 0.69     | 0.4919     | 2.4969 | 0.69     | 0.6317   | 0.3029 | 0.1260 |


### 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_t2.5_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