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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t1.5_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: 0.8009
- Accuracy: 0.695
- Brier Loss: 0.4518
- Nll: 2.3840
- F1 Micro: 0.695
- F1 Macro: 0.6406
- Ece: 0.2661
- Aurc: 0.1211

## 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   | 1.7971          | 0.17     | 0.8966     | 8.4593 | 0.17     | 0.1148   | 0.2202 | 0.8308 |
| No log        | 2.0   | 26   | 1.7887          | 0.13     | 0.8956     | 8.3211 | 0.13     | 0.0772   | 0.2024 | 0.8359 |
| No log        | 3.0   | 39   | 1.7450          | 0.225    | 0.8868     | 6.4554 | 0.225    | 0.1165   | 0.2502 | 0.7572 |
| No log        | 4.0   | 52   | 1.6811          | 0.24     | 0.8733     | 5.9510 | 0.24     | 0.0953   | 0.2651 | 0.6944 |
| No log        | 5.0   | 65   | 1.6411          | 0.19     | 0.8649     | 6.0993 | 0.19     | 0.0493   | 0.2422 | 0.7783 |
| No log        | 6.0   | 78   | 1.5475          | 0.195    | 0.8429     | 6.2065 | 0.195    | 0.0630   | 0.2472 | 0.7110 |
| No log        | 7.0   | 91   | 1.4688          | 0.3      | 0.8197     | 6.0345 | 0.3      | 0.1481   | 0.2936 | 0.5379 |
| No log        | 8.0   | 104  | 1.5036          | 0.285    | 0.8294     | 5.6660 | 0.285    | 0.1428   | 0.2869 | 0.6535 |
| No log        | 9.0   | 117  | 1.3901          | 0.34     | 0.7934     | 5.9107 | 0.34     | 0.1853   | 0.2894 | 0.5277 |
| No log        | 10.0  | 130  | 1.3484          | 0.37     | 0.7760     | 5.6441 | 0.37     | 0.2175   | 0.3177 | 0.5266 |
| No log        | 11.0  | 143  | 1.3375          | 0.34     | 0.7734     | 5.0872 | 0.34     | 0.2083   | 0.2902 | 0.5557 |
| No log        | 12.0  | 156  | 1.3639          | 0.305    | 0.7834     | 4.5070 | 0.305    | 0.1885   | 0.2674 | 0.6177 |
| No log        | 13.0  | 169  | 1.2321          | 0.415    | 0.7225     | 4.3464 | 0.415    | 0.2751   | 0.2943 | 0.3825 |
| No log        | 14.0  | 182  | 1.1453          | 0.44     | 0.6767     | 4.4158 | 0.44     | 0.2864   | 0.2617 | 0.3413 |
| No log        | 15.0  | 195  | 1.1830          | 0.43     | 0.6965     | 3.8251 | 0.4300   | 0.2972   | 0.2912 | 0.4239 |
| No log        | 16.0  | 208  | 1.0572          | 0.535    | 0.6230     | 3.5943 | 0.535    | 0.3758   | 0.2861 | 0.2291 |
| No log        | 17.0  | 221  | 1.0532          | 0.585    | 0.6151     | 3.3834 | 0.585    | 0.4331   | 0.3278 | 0.1879 |
| No log        | 18.0  | 234  | 1.0940          | 0.565    | 0.6374     | 3.2290 | 0.565    | 0.4431   | 0.3313 | 0.2415 |
| No log        | 19.0  | 247  | 0.9877          | 0.585    | 0.5886     | 3.1068 | 0.585    | 0.4564   | 0.2896 | 0.2110 |
| No log        | 20.0  | 260  | 1.0405          | 0.61     | 0.6056     | 3.1786 | 0.61     | 0.5038   | 0.3428 | 0.1962 |
| No log        | 21.0  | 273  | 0.9728          | 0.635    | 0.5634     | 2.9133 | 0.635    | 0.5293   | 0.3333 | 0.1664 |
| No log        | 22.0  | 286  | 0.9425          | 0.635    | 0.5527     | 2.8909 | 0.635    | 0.5237   | 0.3131 | 0.1796 |
| No log        | 23.0  | 299  | 0.9549          | 0.65     | 0.5605     | 2.8074 | 0.65     | 0.5539   | 0.3283 | 0.1914 |
| No log        | 24.0  | 312  | 1.0085          | 0.67     | 0.5733     | 2.8377 | 0.67     | 0.5543   | 0.3525 | 0.1571 |
| No log        | 25.0  | 325  | 0.9140          | 0.655    | 0.5257     | 2.5878 | 0.655    | 0.5603   | 0.3171 | 0.1495 |
| No log        | 26.0  | 338  | 0.8979          | 0.65     | 0.5249     | 2.7723 | 0.65     | 0.5563   | 0.2843 | 0.1646 |
| No log        | 27.0  | 351  | 0.8912          | 0.675    | 0.5082     | 2.6562 | 0.675    | 0.5837   | 0.2871 | 0.1380 |
| No log        | 28.0  | 364  | 0.8966          | 0.66     | 0.5242     | 2.3150 | 0.66     | 0.5890   | 0.3180 | 0.1777 |
| No log        | 29.0  | 377  | 0.8602          | 0.67     | 0.4959     | 2.5813 | 0.67     | 0.5866   | 0.3023 | 0.1319 |
| No log        | 30.0  | 390  | 0.8434          | 0.69     | 0.4779     | 2.5451 | 0.69     | 0.6130   | 0.3061 | 0.1188 |
| No log        | 31.0  | 403  | 0.8406          | 0.715    | 0.4782     | 2.3339 | 0.715    | 0.6438   | 0.3241 | 0.1092 |
| No log        | 32.0  | 416  | 0.8294          | 0.71     | 0.4726     | 2.5394 | 0.7100   | 0.6308   | 0.2922 | 0.1218 |
| No log        | 33.0  | 429  | 0.8329          | 0.68     | 0.4763     | 2.4520 | 0.68     | 0.6166   | 0.2592 | 0.1396 |
| No log        | 34.0  | 442  | 0.8937          | 0.69     | 0.5015     | 2.5649 | 0.69     | 0.6357   | 0.3293 | 0.1279 |
| No log        | 35.0  | 455  | 0.8358          | 0.665    | 0.4807     | 2.4437 | 0.665    | 0.6178   | 0.2380 | 0.1473 |
| No log        | 36.0  | 468  | 0.8283          | 0.685    | 0.4747     | 2.5408 | 0.685    | 0.6304   | 0.3126 | 0.1361 |
| No log        | 37.0  | 481  | 0.8235          | 0.685    | 0.4707     | 2.4620 | 0.685    | 0.6300   | 0.2757 | 0.1343 |
| No log        | 38.0  | 494  | 0.8289          | 0.68     | 0.4778     | 2.5443 | 0.68     | 0.6305   | 0.2935 | 0.1469 |
| 0.9462        | 39.0  | 507  | 0.8373          | 0.69     | 0.4728     | 2.5775 | 0.69     | 0.6281   | 0.3028 | 0.1149 |
| 0.9462        | 40.0  | 520  | 0.8062          | 0.715    | 0.4548     | 2.3673 | 0.715    | 0.6587   | 0.2776 | 0.1133 |
| 0.9462        | 41.0  | 533  | 0.7990          | 0.705    | 0.4517     | 2.3284 | 0.705    | 0.6463   | 0.2716 | 0.1185 |
| 0.9462        | 42.0  | 546  | 0.8210          | 0.7      | 0.4650     | 2.5646 | 0.7      | 0.6432   | 0.2690 | 0.1199 |
| 0.9462        | 43.0  | 559  | 0.8102          | 0.695    | 0.4558     | 2.5651 | 0.695    | 0.6442   | 0.2656 | 0.1184 |
| 0.9462        | 44.0  | 572  | 0.8061          | 0.69     | 0.4566     | 2.5154 | 0.69     | 0.6356   | 0.2816 | 0.1267 |
| 0.9462        | 45.0  | 585  | 0.8018          | 0.7      | 0.4531     | 2.4982 | 0.7      | 0.6419   | 0.2696 | 0.1192 |
| 0.9462        | 46.0  | 598  | 0.8040          | 0.7      | 0.4521     | 2.5309 | 0.7      | 0.6448   | 0.2797 | 0.1166 |
| 0.9462        | 47.0  | 611  | 0.8062          | 0.68     | 0.4560     | 2.5452 | 0.68     | 0.6370   | 0.2744 | 0.1217 |
| 0.9462        | 48.0  | 624  | 0.8011          | 0.69     | 0.4529     | 2.4281 | 0.69     | 0.6402   | 0.2594 | 0.1224 |
| 0.9462        | 49.0  | 637  | 0.8017          | 0.69     | 0.4532     | 2.4239 | 0.69     | 0.6400   | 0.2613 | 0.1261 |
| 0.9462        | 50.0  | 650  | 0.8009          | 0.695    | 0.4518     | 2.3840 | 0.695    | 0.6406   | 0.2661 | 0.1211 |


### 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_a0.7", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us