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# dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5443
- Accuracy: 0.8293
- Brier Loss: 0.2695
- Nll: 1.2500
- F1 Micro: 0.8293
- F1 Macro: 0.8301
- Ece: 0.0874
- Aurc: 0.0693

## 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: 96
- eval_batch_size: 96
- 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   | 167  | 1.1672          | 0.6082   | 0.5198     | 2.4714 | 0.6082   | 0.6021   | 0.0681 | 0.1731 |
| No log        | 2.0   | 334  | 0.8928          | 0.7177   | 0.4028     | 2.0687 | 0.7178   | 0.7161   | 0.0715 | 0.0997 |
| 1.1617        | 3.0   | 501  | 0.7584          | 0.7602   | 0.3454     | 1.8041 | 0.7602   | 0.7637   | 0.0762 | 0.0747 |
| 1.1617        | 4.0   | 668  | 0.7048          | 0.768    | 0.3262     | 1.6695 | 0.768    | 0.7687   | 0.0550 | 0.0687 |
| 1.1617        | 5.0   | 835  | 0.6921          | 0.7745   | 0.3214     | 1.6428 | 0.7745   | 0.7683   | 0.0477 | 0.0738 |
| 0.4396        | 6.0   | 1002 | 0.6616          | 0.789    | 0.3105     | 1.5655 | 0.7890   | 0.7897   | 0.0586 | 0.0714 |
| 0.4396        | 7.0   | 1169 | 0.6363          | 0.794    | 0.3001     | 1.5694 | 0.7940   | 0.7970   | 0.0549 | 0.0674 |
| 0.4396        | 8.0   | 1336 | 0.6753          | 0.7792   | 0.3259     | 1.4975 | 0.7792   | 0.7804   | 0.0647 | 0.0777 |
| 0.2291        | 9.0   | 1503 | 0.6247          | 0.8025   | 0.2979     | 1.4968 | 0.8025   | 0.8037   | 0.0705 | 0.0669 |
| 0.2291        | 10.0  | 1670 | 0.6347          | 0.799    | 0.3032     | 1.4834 | 0.799    | 0.8011   | 0.0720 | 0.0743 |
| 0.2291        | 11.0  | 1837 | 0.6328          | 0.7975   | 0.3045     | 1.4998 | 0.7975   | 0.8031   | 0.0773 | 0.0659 |
| 0.1575        | 12.0  | 2004 | 0.6442          | 0.7965   | 0.3097     | 1.4447 | 0.7965   | 0.7979   | 0.0714 | 0.0824 |
| 0.1575        | 13.0  | 2171 | 0.6354          | 0.8013   | 0.3043     | 1.4874 | 0.8013   | 0.8035   | 0.0712 | 0.0741 |
| 0.1575        | 14.0  | 2338 | 0.6443          | 0.799    | 0.3091     | 1.5848 | 0.799    | 0.8022   | 0.0791 | 0.0859 |
| 0.1285        | 15.0  | 2505 | 0.6357          | 0.8017   | 0.3042     | 1.5670 | 0.8017   | 0.8002   | 0.0799 | 0.0685 |
| 0.1285        | 16.0  | 2672 | 0.6166          | 0.807    | 0.2965     | 1.4806 | 0.807    | 0.8056   | 0.0720 | 0.0745 |
| 0.1285        | 17.0  | 2839 | 0.6433          | 0.7993   | 0.3159     | 1.5024 | 0.7993   | 0.8023   | 0.0805 | 0.0857 |
| 0.1121        | 18.0  | 3006 | 0.6102          | 0.8147   | 0.2960     | 1.4550 | 0.8148   | 0.8144   | 0.0775 | 0.0698 |
| 0.1121        | 19.0  | 3173 | 0.6616          | 0.7995   | 0.3146     | 1.6009 | 0.7995   | 0.7962   | 0.0892 | 0.0883 |
| 0.1121        | 20.0  | 3340 | 0.6163          | 0.8037   | 0.3029     | 1.4525 | 0.8037   | 0.8059   | 0.0920 | 0.0771 |
| 0.1012        | 21.0  | 3507 | 0.6186          | 0.8093   | 0.3017     | 1.5539 | 0.8093   | 0.8111   | 0.0920 | 0.0712 |
| 0.1012        | 22.0  | 3674 | 0.5982          | 0.8137   | 0.2930     | 1.4533 | 0.8137   | 0.8140   | 0.0815 | 0.0668 |
| 0.1012        | 23.0  | 3841 | 0.5928          | 0.822    | 0.2864     | 1.4312 | 0.822    | 0.8218   | 0.0723 | 0.0818 |
| 0.0888        | 24.0  | 4008 | 0.5931          | 0.8135   | 0.2900     | 1.4129 | 0.8135   | 0.8143   | 0.0894 | 0.0706 |
| 0.0888        | 25.0  | 4175 | 0.5807          | 0.8183   | 0.2849     | 1.4241 | 0.8183   | 0.8203   | 0.0903 | 0.0683 |
| 0.0888        | 26.0  | 4342 | 0.5859          | 0.8193   | 0.2869     | 1.4385 | 0.8193   | 0.8194   | 0.0879 | 0.0698 |
| 0.0828        | 27.0  | 4509 | 0.5957          | 0.8147   | 0.2941     | 1.4132 | 0.8148   | 0.8151   | 0.0847 | 0.0732 |
| 0.0828        | 28.0  | 4676 | 0.5791          | 0.818    | 0.2852     | 1.4231 | 0.818    | 0.8185   | 0.0896 | 0.0612 |
| 0.0828        | 29.0  | 4843 | 0.5888          | 0.8137   | 0.2895     | 1.3998 | 0.8137   | 0.8148   | 0.0925 | 0.0740 |
| 0.0776        | 30.0  | 5010 | 0.5633          | 0.8225   | 0.2798     | 1.3391 | 0.8225   | 0.8234   | 0.0878 | 0.0760 |
| 0.0776        | 31.0  | 5177 | 0.5635          | 0.8247   | 0.2785     | 1.3193 | 0.8247   | 0.8256   | 0.0900 | 0.0587 |
| 0.0776        | 32.0  | 5344 | 0.5580          | 0.8223   | 0.2784     | 1.2970 | 0.8223   | 0.8241   | 0.0905 | 0.0704 |
| 0.0727        | 33.0  | 5511 | 0.5502          | 0.826    | 0.2724     | 1.2733 | 0.826    | 0.8268   | 0.0865 | 0.0619 |
| 0.0727        | 34.0  | 5678 | 0.5448          | 0.8293   | 0.2720     | 1.2237 | 0.8293   | 0.8303   | 0.0820 | 0.0639 |
| 0.0727        | 35.0  | 5845 | 0.5480          | 0.8257   | 0.2729     | 1.2867 | 0.8257   | 0.8271   | 0.0928 | 0.0586 |
| 0.0696        | 36.0  | 6012 | 0.5437          | 0.8293   | 0.2703     | 1.2427 | 0.8293   | 0.8298   | 0.0871 | 0.0630 |
| 0.0696        | 37.0  | 6179 | 0.5460          | 0.8253   | 0.2712     | 1.2629 | 0.8253   | 0.8262   | 0.0912 | 0.0598 |
| 0.0696        | 38.0  | 6346 | 0.5425          | 0.8295   | 0.2703     | 1.2440 | 0.8295   | 0.8303   | 0.0899 | 0.0611 |
| 0.0677        | 39.0  | 6513 | 0.5421          | 0.8307   | 0.2690     | 1.2453 | 0.8308   | 0.8319   | 0.0835 | 0.0665 |
| 0.0677        | 40.0  | 6680 | 0.5406          | 0.8287   | 0.2689     | 1.2465 | 0.8287   | 0.8296   | 0.0895 | 0.0612 |
| 0.0677        | 41.0  | 6847 | 0.5423          | 0.8277   | 0.2696     | 1.2735 | 0.8277   | 0.8284   | 0.0893 | 0.0604 |
| 0.0663        | 42.0  | 7014 | 0.5406          | 0.8297   | 0.2676     | 1.2403 | 0.8297   | 0.8306   | 0.0894 | 0.0657 |
| 0.0663        | 43.0  | 7181 | 0.5410          | 0.8313   | 0.2686     | 1.2359 | 0.8313   | 0.8323   | 0.0895 | 0.0635 |
| 0.0663        | 44.0  | 7348 | 0.5416          | 0.8287   | 0.2685     | 1.2308 | 0.8287   | 0.8295   | 0.0883 | 0.0647 |
| 0.0652        | 45.0  | 7515 | 0.5431          | 0.8275   | 0.2697     | 1.2374 | 0.8275   | 0.8282   | 0.0932 | 0.0648 |
| 0.0652        | 46.0  | 7682 | 0.5433          | 0.8295   | 0.2693     | 1.2347 | 0.8295   | 0.8303   | 0.0891 | 0.0681 |
| 0.0652        | 47.0  | 7849 | 0.5441          | 0.8277   | 0.2696     | 1.2433 | 0.8277   | 0.8286   | 0.0882 | 0.0681 |
| 0.0651        | 48.0  | 8016 | 0.5439          | 0.8293   | 0.2695     | 1.2358 | 0.8293   | 0.8301   | 0.0888 | 0.0692 |
| 0.0651        | 49.0  | 8183 | 0.5445          | 0.8287   | 0.2696     | 1.2499 | 0.8287   | 0.8296   | 0.0882 | 0.0695 |
| 0.0651        | 50.0  | 8350 | 0.5443          | 0.8293   | 0.2695     | 1.2500 | 0.8293   | 0.8301   | 0.0874 | 0.0693 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

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