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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_simkd

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: 0.0540
- Accuracy: 0.859
- Brier Loss: 0.2977
- Nll: 1.1492
- F1 Micro: 0.859
- F1 Macro: 0.8598
- Ece: 0.2784
- Aurc: 0.0325

## 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: 16
- eval_batch_size: 16
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0787        | 1.0   | 1000  | 0.0770          | 0.2978   | 0.9091     | 2.9717 | 0.2978   | 0.2516   | 0.2150 | 0.5174 |
| 0.0697        | 2.0   | 2000  | 0.0679          | 0.6372   | 0.6886     | 1.8065 | 0.6372   | 0.6302   | 0.4021 | 0.1388 |
| 0.0655        | 3.0   | 3000  | 0.0645          | 0.7492   | 0.5738     | 1.7388 | 0.7492   | 0.7460   | 0.4215 | 0.0921 |
| 0.0628        | 4.0   | 4000  | 0.0631          | 0.752    | 0.5394     | 1.7446 | 0.752    | 0.7551   | 0.3922 | 0.0837 |
| 0.0611        | 5.0   | 5000  | 0.0612          | 0.768    | 0.4928     | 1.5830 | 0.768    | 0.7700   | 0.3710 | 0.0655 |
| 0.0593        | 6.0   | 6000  | 0.0609          | 0.7598   | 0.4655     | 1.5730 | 0.7598   | 0.7667   | 0.3228 | 0.0802 |
| 0.0578        | 7.0   | 7000  | 0.0585          | 0.8063   | 0.4195     | 1.4053 | 0.8062   | 0.8065   | 0.3459 | 0.0521 |
| 0.0566        | 8.0   | 8000  | 0.0581          | 0.8073   | 0.3997     | 1.2957 | 0.8073   | 0.8084   | 0.3207 | 0.0538 |
| 0.0557        | 9.0   | 9000  | 0.0571          | 0.8287   | 0.3810     | 1.3269 | 0.8287   | 0.8301   | 0.3307 | 0.0473 |
| 0.0554        | 10.0  | 10000 | 0.0573          | 0.8115   | 0.3780     | 1.3469 | 0.8115   | 0.8128   | 0.3011 | 0.0508 |
| 0.0546        | 11.0  | 11000 | 0.0563          | 0.8395   | 0.3549     | 1.2882 | 0.8395   | 0.8401   | 0.3197 | 0.0386 |
| 0.0541        | 12.0  | 12000 | 0.0558          | 0.839    | 0.3426     | 1.2653 | 0.839    | 0.8401   | 0.3014 | 0.0394 |
| 0.0536        | 13.0  | 13000 | 0.0553          | 0.8465   | 0.3259     | 1.1941 | 0.8465   | 0.8473   | 0.2980 | 0.0357 |
| 0.0537        | 14.0  | 14000 | 0.0559          | 0.8303   | 0.3499     | 1.2460 | 0.8303   | 0.8338   | 0.2955 | 0.0427 |
| 0.0532        | 15.0  | 15000 | 0.0551          | 0.8445   | 0.3296     | 1.1799 | 0.8445   | 0.8453   | 0.2990 | 0.0360 |
| 0.0529        | 16.0  | 16000 | 0.0549          | 0.845    | 0.3224     | 1.1801 | 0.845    | 0.8456   | 0.2895 | 0.0364 |
| 0.0527        | 17.0  | 17000 | 0.0549          | 0.849    | 0.3264     | 1.1725 | 0.849    | 0.8503   | 0.2991 | 0.0363 |
| 0.0526        | 18.0  | 18000 | 0.0547          | 0.8518   | 0.3170     | 1.1755 | 0.8518   | 0.8527   | 0.2943 | 0.0334 |
| 0.0524        | 19.0  | 19000 | 0.0546          | 0.8458   | 0.3213     | 1.1417 | 0.8458   | 0.8466   | 0.2917 | 0.0344 |
| 0.0522        | 20.0  | 20000 | 0.0544          | 0.8545   | 0.3105     | 1.1512 | 0.8545   | 0.8542   | 0.2891 | 0.0333 |
| 0.052         | 21.0  | 21000 | 0.0542          | 0.855    | 0.3120     | 1.1403 | 0.855    | 0.8555   | 0.2940 | 0.0333 |
| 0.0518        | 22.0  | 22000 | 0.0542          | 0.854    | 0.3096     | 1.1533 | 0.854    | 0.8545   | 0.2893 | 0.0319 |
| 0.0517        | 23.0  | 23000 | 0.0541          | 0.8545   | 0.3098     | 1.1445 | 0.8545   | 0.8556   | 0.2920 | 0.0315 |
| 0.0516        | 24.0  | 24000 | 0.0540          | 0.8578   | 0.3097     | 1.1273 | 0.8578   | 0.8586   | 0.2958 | 0.0315 |
| 0.0514        | 25.0  | 25000 | 0.0540          | 0.8532   | 0.3076     | 1.1579 | 0.8532   | 0.8533   | 0.2849 | 0.0342 |
| 0.0513        | 26.0  | 26000 | 0.0540          | 0.855    | 0.3055     | 1.1269 | 0.855    | 0.8563   | 0.2855 | 0.0325 |
| 0.0511        | 27.0  | 27000 | 0.0538          | 0.8565   | 0.3029     | 1.1571 | 0.8565   | 0.8572   | 0.2827 | 0.0334 |
| 0.051         | 28.0  | 28000 | 0.0538          | 0.8598   | 0.3012     | 1.1409 | 0.8598   | 0.8604   | 0.2851 | 0.0317 |
| 0.0509        | 29.0  | 29000 | 0.0537          | 0.86     | 0.3003     | 1.1525 | 0.8600   | 0.8603   | 0.2839 | 0.0323 |
| 0.0508        | 30.0  | 30000 | 0.0537          | 0.8575   | 0.3024     | 1.1430 | 0.8575   | 0.8585   | 0.2849 | 0.0319 |
| 0.0507        | 31.0  | 31000 | 0.0537          | 0.8595   | 0.3015     | 1.1454 | 0.8595   | 0.8603   | 0.2859 | 0.0311 |
| 0.0507        | 32.0  | 32000 | 0.0537          | 0.8598   | 0.3005     | 1.1463 | 0.8598   | 0.8603   | 0.2847 | 0.0316 |
| 0.0506        | 33.0  | 33000 | 0.0537          | 0.8598   | 0.2966     | 1.1392 | 0.8598   | 0.8605   | 0.2800 | 0.0309 |
| 0.0506        | 34.0  | 34000 | 0.0537          | 0.8562   | 0.3018     | 1.1442 | 0.8562   | 0.8574   | 0.2813 | 0.0327 |
| 0.0505        | 35.0  | 35000 | 0.0537          | 0.855    | 0.2995     | 1.1402 | 0.855    | 0.8556   | 0.2790 | 0.0324 |
| 0.0505        | 36.0  | 36000 | 0.0537          | 0.8575   | 0.2980     | 1.1324 | 0.8575   | 0.8582   | 0.2783 | 0.0314 |
| 0.0504        | 37.0  | 37000 | 0.0538          | 0.8562   | 0.2981     | 1.1429 | 0.8562   | 0.8570   | 0.2770 | 0.0320 |
| 0.0503        | 38.0  | 38000 | 0.0538          | 0.8565   | 0.2997     | 1.1319 | 0.8565   | 0.8573   | 0.2795 | 0.0324 |
| 0.0503        | 39.0  | 39000 | 0.0538          | 0.857    | 0.2988     | 1.1447 | 0.857    | 0.8578   | 0.2791 | 0.0320 |
| 0.0502        | 40.0  | 40000 | 0.0538          | 0.8588   | 0.2982     | 1.1409 | 0.8588   | 0.8595   | 0.2798 | 0.0320 |
| 0.0502        | 41.0  | 41000 | 0.0538          | 0.8572   | 0.2982     | 1.1455 | 0.8572   | 0.8580   | 0.2781 | 0.0319 |
| 0.0502        | 42.0  | 42000 | 0.0538          | 0.8602   | 0.2979     | 1.1357 | 0.8602   | 0.8609   | 0.2809 | 0.0320 |
| 0.0501        | 43.0  | 43000 | 0.0539          | 0.8568   | 0.2987     | 1.1462 | 0.8568   | 0.8574   | 0.2787 | 0.0322 |
| 0.0501        | 44.0  | 44000 | 0.0539          | 0.8595   | 0.2974     | 1.1456 | 0.8595   | 0.8602   | 0.2789 | 0.0322 |
| 0.0501        | 45.0  | 45000 | 0.0539          | 0.8592   | 0.2980     | 1.1460 | 0.8592   | 0.8601   | 0.2792 | 0.0322 |
| 0.05          | 46.0  | 46000 | 0.0539          | 0.8588   | 0.2979     | 1.1441 | 0.8588   | 0.8596   | 0.2787 | 0.0322 |
| 0.05          | 47.0  | 47000 | 0.0540          | 0.8592   | 0.2983     | 1.1501 | 0.8592   | 0.8600   | 0.2793 | 0.0324 |
| 0.05          | 48.0  | 48000 | 0.0540          | 0.8588   | 0.2980     | 1.1462 | 0.8588   | 0.8595   | 0.2787 | 0.0324 |
| 0.05          | 49.0  | 49000 | 0.0540          | 0.8598   | 0.2978     | 1.1507 | 0.8598   | 0.8604   | 0.2793 | 0.0324 |
| 0.05          | 50.0  | 50000 | 0.0540          | 0.859    | 0.2977     | 1.1492 | 0.859    | 0.8598   | 0.2784 | 0.0325 |


### Framework versions

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

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