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# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t2.5_a0.9

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: 0.8672
- Accuracy: 0.71
- Brier Loss: 0.4047
- Nll: 2.1924
- F1 Micro: 0.7100
- F1 Macro: 0.6463
- Ece: 0.2420
- Aurc: 0.1050

## 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   | 2.1239          | 0.16     | 0.8967     | 8.4233 | 0.16     | 0.1062   | 0.2101 | 0.8304 |
| No log        | 2.0   | 26   | 2.1201          | 0.14     | 0.8961     | 8.2220 | 0.14     | 0.0876   | 0.1970 | 0.8491 |
| No log        | 3.0   | 39   | 2.0724          | 0.215    | 0.8865     | 6.2039 | 0.2150   | 0.1169   | 0.2432 | 0.7837 |
| No log        | 4.0   | 52   | 2.0291          | 0.185    | 0.8773     | 5.6169 | 0.185    | 0.0792   | 0.2329 | 0.7651 |
| No log        | 5.0   | 65   | 1.9592          | 0.215    | 0.8614     | 6.0237 | 0.2150   | 0.0835   | 0.2493 | 0.7373 |
| No log        | 6.0   | 78   | 1.9039          | 0.205    | 0.8483     | 5.9575 | 0.205    | 0.0619   | 0.2493 | 0.7526 |
| No log        | 7.0   | 91   | 1.8651          | 0.26     | 0.8381     | 5.6215 | 0.26     | 0.1490   | 0.2663 | 0.6747 |
| No log        | 8.0   | 104  | 1.8342          | 0.225    | 0.8311     | 5.7631 | 0.225    | 0.1071   | 0.2425 | 0.6919 |
| No log        | 9.0   | 117  | 1.8057          | 0.31     | 0.8218     | 5.2969 | 0.31     | 0.2118   | 0.2795 | 0.6489 |
| No log        | 10.0  | 130  | 1.5737          | 0.46     | 0.7277     | 5.1748 | 0.46     | 0.2853   | 0.3279 | 0.2977 |
| No log        | 11.0  | 143  | 1.5629          | 0.415    | 0.7331     | 4.8259 | 0.415    | 0.2846   | 0.2924 | 0.3880 |
| No log        | 12.0  | 156  | 1.5283          | 0.45     | 0.7135     | 4.0012 | 0.45     | 0.3122   | 0.3298 | 0.3197 |
| No log        | 13.0  | 169  | 1.4200          | 0.51     | 0.6674     | 3.9849 | 0.51     | 0.3400   | 0.3259 | 0.2549 |
| No log        | 14.0  | 182  | 1.4334          | 0.535    | 0.6710     | 3.7006 | 0.535    | 0.3840   | 0.3291 | 0.2584 |
| No log        | 15.0  | 195  | 1.4306          | 0.45     | 0.6854     | 3.8260 | 0.45     | 0.3120   | 0.3055 | 0.4297 |
| No log        | 16.0  | 208  | 1.3175          | 0.585    | 0.6174     | 3.3484 | 0.585    | 0.4401   | 0.3406 | 0.1916 |
| No log        | 17.0  | 221  | 1.2680          | 0.57     | 0.5998     | 3.1408 | 0.57     | 0.4356   | 0.2903 | 0.2136 |
| No log        | 18.0  | 234  | 1.2605          | 0.58     | 0.6020     | 3.2085 | 0.58     | 0.4711   | 0.2915 | 0.2355 |
| No log        | 19.0  | 247  | 1.2292          | 0.61     | 0.5791     | 3.0633 | 0.61     | 0.5021   | 0.2929 | 0.2082 |
| No log        | 20.0  | 260  | 1.3872          | 0.54     | 0.6604     | 3.2778 | 0.54     | 0.4604   | 0.3284 | 0.3506 |
| No log        | 21.0  | 273  | 1.1646          | 0.625    | 0.5520     | 2.8539 | 0.625    | 0.5193   | 0.2828 | 0.1885 |
| No log        | 22.0  | 286  | 1.1565          | 0.655    | 0.5438     | 2.6915 | 0.655    | 0.5437   | 0.3430 | 0.1549 |
| No log        | 23.0  | 299  | 1.1041          | 0.625    | 0.5298     | 2.9930 | 0.625    | 0.5241   | 0.2423 | 0.1906 |
| No log        | 24.0  | 312  | 1.0448          | 0.685    | 0.4895     | 2.8196 | 0.685    | 0.5846   | 0.2701 | 0.1411 |
| No log        | 25.0  | 325  | 1.0623          | 0.695    | 0.4904     | 2.6903 | 0.695    | 0.6086   | 0.2762 | 0.1435 |
| No log        | 26.0  | 338  | 0.9872          | 0.695    | 0.4607     | 2.6336 | 0.695    | 0.5953   | 0.2728 | 0.1180 |
| No log        | 27.0  | 351  | 0.9789          | 0.705    | 0.4580     | 2.6326 | 0.705    | 0.6127   | 0.2579 | 0.1171 |
| No log        | 28.0  | 364  | 1.0033          | 0.685    | 0.4707     | 2.5747 | 0.685    | 0.5906   | 0.2747 | 0.1291 |
| No log        | 29.0  | 377  | 1.0152          | 0.7      | 0.4789     | 2.4333 | 0.7      | 0.6260   | 0.2951 | 0.1739 |
| No log        | 30.0  | 390  | 1.0107          | 0.715    | 0.4684     | 2.5194 | 0.715    | 0.6401   | 0.3197 | 0.1389 |
| No log        | 31.0  | 403  | 0.9511          | 0.69     | 0.4445     | 2.5648 | 0.69     | 0.6131   | 0.2648 | 0.1298 |
| No log        | 32.0  | 416  | 0.9586          | 0.735    | 0.4448     | 2.3342 | 0.735    | 0.6578   | 0.2941 | 0.1275 |
| No log        | 33.0  | 429  | 1.0010          | 0.73     | 0.4625     | 2.4748 | 0.7300   | 0.6613   | 0.3307 | 0.1202 |
| No log        | 34.0  | 442  | 0.9481          | 0.71     | 0.4361     | 2.4986 | 0.7100   | 0.6456   | 0.2856 | 0.1228 |
| No log        | 35.0  | 455  | 0.9190          | 0.69     | 0.4323     | 2.6586 | 0.69     | 0.6265   | 0.2538 | 0.1250 |
| No log        | 36.0  | 468  | 0.9226          | 0.715    | 0.4350     | 2.2652 | 0.715    | 0.6507   | 0.2868 | 0.1328 |
| No log        | 37.0  | 481  | 0.9017          | 0.725    | 0.4182     | 2.5141 | 0.7250   | 0.6590   | 0.2547 | 0.1013 |
| No log        | 38.0  | 494  | 0.9092          | 0.72     | 0.4218     | 2.5171 | 0.72     | 0.6495   | 0.2677 | 0.1055 |
| 1.0958        | 39.0  | 507  | 0.9093          | 0.71     | 0.4221     | 2.6479 | 0.7100   | 0.6456   | 0.2567 | 0.1185 |
| 1.0958        | 40.0  | 520  | 0.8926          | 0.71     | 0.4204     | 2.3785 | 0.7100   | 0.6522   | 0.2396 | 0.1153 |
| 1.0958        | 41.0  | 533  | 0.8928          | 0.715    | 0.4157     | 2.5719 | 0.715    | 0.6487   | 0.2708 | 0.1067 |
| 1.0958        | 42.0  | 546  | 0.8967          | 0.715    | 0.4247     | 2.6422 | 0.715    | 0.6495   | 0.2525 | 0.1174 |
| 1.0958        | 43.0  | 559  | 0.8773          | 0.695    | 0.4116     | 2.5548 | 0.695    | 0.6400   | 0.2491 | 0.1142 |
| 1.0958        | 44.0  | 572  | 0.8660          | 0.71     | 0.4036     | 2.2950 | 0.7100   | 0.6535   | 0.2401 | 0.1009 |
| 1.0958        | 45.0  | 585  | 0.8718          | 0.72     | 0.4057     | 2.4922 | 0.72     | 0.6551   | 0.2624 | 0.0998 |
| 1.0958        | 46.0  | 598  | 0.8737          | 0.7      | 0.4070     | 2.4455 | 0.7      | 0.6416   | 0.2360 | 0.1052 |
| 1.0958        | 47.0  | 611  | 0.8707          | 0.715    | 0.4094     | 2.3519 | 0.715    | 0.6494   | 0.2514 | 0.1086 |
| 1.0958        | 48.0  | 624  | 0.8640          | 0.705    | 0.4039     | 2.3765 | 0.705    | 0.6430   | 0.2538 | 0.1041 |
| 1.0958        | 49.0  | 637  | 0.8702          | 0.7      | 0.4066     | 2.5524 | 0.7      | 0.6423   | 0.2160 | 0.1080 |
| 1.0958        | 50.0  | 650  | 0.8672          | 0.71     | 0.4047     | 2.1924 | 0.7100   | 0.6463   | 0.2420 | 0.1050 |


### 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_a0.9", "results": []}]}
transformers pytorch resnet image-classification generated_from_trainer base_model:microsoft/resnet-50 license:apache-2.0 autotrain_compatible endpoints_compatible region:us