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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_og_simkd_rand

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 267.1454
- Accuracy: 0.6807
- Brier Loss: 0.6059
- Nll: 2.5092
- F1 Micro: 0.6807
- F1 Macro: 0.6792
- Ece: 0.2988
- Aurc: 0.1779

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 286.6037      | 1.0   | 1000  | 286.3978        | 0.242    | 1.0585     | 5.0180 | 0.242    | 0.1919   | 0.3885 | 0.6070 |
| 284.5917      | 2.0   | 2000  | 285.0526        | 0.235    | 1.4192     | 6.2048 | 0.235    | 0.1678   | 0.6914 | 0.6366 |
| 284.1567      | 3.0   | 3000  | 283.4989        | 0.3705   | 1.0456     | 4.7503 | 0.3705   | 0.2880   | 0.4669 | 0.4145 |
| 282.6679      | 4.0   | 4000  | 282.5618        | 0.4042   | 0.8940     | 4.1927 | 0.4042   | 0.3644   | 0.3629 | 0.3572 |
| 282.2283      | 5.0   | 5000  | 281.9135        | 0.418    | 0.9976     | 3.8856 | 0.418    | 0.3686   | 0.4631 | 0.3778 |
| 281.3193      | 6.0   | 6000  | 279.9180        | 0.4723   | 0.8755     | 3.4852 | 0.4723   | 0.4312   | 0.3960 | 0.2962 |
| 280.7993      | 7.0   | 7000  | 279.2325        | 0.5038   | 0.8411     | 3.3760 | 0.5038   | 0.4635   | 0.3844 | 0.2753 |
| 279.8249      | 8.0   | 8000  | 278.4682        | 0.5268   | 0.8078     | 3.1572 | 0.5268   | 0.4894   | 0.3705 | 0.2620 |
| 278.8243      | 9.0   | 9000  | 278.2146        | 0.5268   | 0.8245     | 3.2631 | 0.5268   | 0.5043   | 0.3819 | 0.2729 |
| 278.1676      | 10.0  | 10000 | 276.9399        | 0.5607   | 0.7853     | 3.0151 | 0.5607   | 0.5390   | 0.3741 | 0.2275 |
| 276.8185      | 11.0  | 11000 | 276.3879        | 0.5697   | 0.7659     | 2.9137 | 0.5697   | 0.5520   | 0.3660 | 0.2221 |
| 276.0937      | 12.0  | 12000 | 275.9589        | 0.5777   | 0.7626     | 2.9855 | 0.5777   | 0.5643   | 0.3606 | 0.2360 |
| 276.0743      | 13.0  | 13000 | 275.6118        | 0.5675   | 0.7938     | 3.2975 | 0.5675   | 0.5545   | 0.3852 | 0.2320 |
| 275.008       | 14.0  | 14000 | 275.0585        | 0.6      | 0.7359     | 2.8607 | 0.6      | 0.5861   | 0.3517 | 0.2142 |
| 274.483       | 15.0  | 15000 | 274.0515        | 0.6292   | 0.6738     | 2.7667 | 0.6292   | 0.6262   | 0.3215 | 0.1904 |
| 273.261       | 16.0  | 16000 | 273.7844        | 0.6312   | 0.6819     | 2.7219 | 0.6312   | 0.6296   | 0.3286 | 0.2048 |
| 272.9319      | 17.0  | 17000 | 273.4691        | 0.6198   | 0.7009     | 2.8745 | 0.6198   | 0.6160   | 0.3410 | 0.2134 |
| 272.456       | 18.0  | 18000 | 273.1716        | 0.6195   | 0.7071     | 2.8631 | 0.6195   | 0.6223   | 0.3440 | 0.2140 |
| 272.0481      | 19.0  | 19000 | 272.5084        | 0.6322   | 0.6864     | 2.7598 | 0.6322   | 0.6292   | 0.3362 | 0.2119 |
| 271.0429      | 20.0  | 20000 | 272.1741        | 0.6365   | 0.6830     | 2.8104 | 0.6365   | 0.6300   | 0.3345 | 0.2185 |
| 271.0098      | 21.0  | 21000 | 271.8972        | 0.649    | 0.6569     | 2.8558 | 0.649    | 0.6477   | 0.3221 | 0.2076 |
| 270.1226      | 22.0  | 22000 | 271.3564        | 0.639    | 0.6850     | 3.0353 | 0.639    | 0.6326   | 0.3372 | 0.2275 |
| 269.8644      | 23.0  | 23000 | 271.2604        | 0.6332   | 0.6903     | 2.9472 | 0.6332   | 0.6330   | 0.3400 | 0.2367 |
| 269.6737      | 24.0  | 24000 | 270.9163        | 0.6485   | 0.6622     | 2.8937 | 0.6485   | 0.6477   | 0.3258 | 0.2139 |
| 268.3083      | 25.0  | 25000 | 270.3471        | 0.6528   | 0.6590     | 2.7873 | 0.6528   | 0.6550   | 0.3231 | 0.2228 |
| 268.6058      | 26.0  | 26000 | 270.2531        | 0.659    | 0.6377     | 2.7500 | 0.659    | 0.6599   | 0.3125 | 0.1980 |
| 268.5694      | 27.0  | 27000 | 270.0281        | 0.6535   | 0.6510     | 2.7183 | 0.6535   | 0.6502   | 0.3210 | 0.2112 |
| 267.5742      | 28.0  | 28000 | 269.6303        | 0.664    | 0.6327     | 2.6630 | 0.664    | 0.6619   | 0.3109 | 0.1974 |
| 267.4235      | 29.0  | 29000 | 269.3493        | 0.6607   | 0.6417     | 2.7860 | 0.6607   | 0.6568   | 0.3162 | 0.2074 |
| 267.1017      | 30.0  | 30000 | 269.1249        | 0.675    | 0.6152     | 2.6205 | 0.675    | 0.6760   | 0.3013 | 0.1923 |
| 266.7395      | 31.0  | 31000 | 268.8958        | 0.6685   | 0.6281     | 2.7126 | 0.6685   | 0.6638   | 0.3086 | 0.1943 |
| 266.3374      | 32.0  | 32000 | 268.6245        | 0.6703   | 0.6224     | 2.7028 | 0.6703   | 0.6686   | 0.3065 | 0.1900 |
| 266.3529      | 33.0  | 33000 | 268.4537        | 0.6697   | 0.6240     | 2.6593 | 0.6697   | 0.6683   | 0.3066 | 0.1964 |
| 266.1322      | 34.0  | 34000 | 268.1314        | 0.678    | 0.6096     | 2.6485 | 0.678    | 0.6784   | 0.3008 | 0.1857 |
| 265.3824      | 35.0  | 35000 | 268.1505        | 0.6707   | 0.6242     | 2.5832 | 0.6707   | 0.6696   | 0.3058 | 0.1916 |
| 265.5754      | 36.0  | 36000 | 267.9319        | 0.676    | 0.6155     | 2.6208 | 0.676    | 0.6761   | 0.3014 | 0.1908 |
| 265.6115      | 37.0  | 37000 | 268.0886        | 0.679    | 0.6093     | 2.6068 | 0.679    | 0.6795   | 0.2991 | 0.1796 |
| 264.8437      | 38.0  | 38000 | 267.9896        | 0.6783   | 0.6113     | 2.5873 | 0.6783   | 0.6765   | 0.3000 | 0.1805 |
| 264.8028      | 39.0  | 39000 | 267.5381        | 0.68     | 0.6048     | 2.5007 | 0.68     | 0.6771   | 0.2974 | 0.1771 |
| 264.8063      | 40.0  | 40000 | 267.6070        | 0.6763   | 0.6127     | 2.5359 | 0.6763   | 0.6751   | 0.3030 | 0.1821 |
| 264.7481      | 41.0  | 41000 | 267.4914        | 0.6837   | 0.6000     | 2.5214 | 0.6837   | 0.6809   | 0.2942 | 0.1830 |
| 264.6455      | 42.0  | 42000 | 267.6581        | 0.6857   | 0.5968     | 2.5211 | 0.6857   | 0.6856   | 0.2919 | 0.1741 |
| 264.0388      | 43.0  | 43000 | 267.3815        | 0.6797   | 0.6035     | 2.5123 | 0.6797   | 0.6795   | 0.2973 | 0.1773 |
| 264.3585      | 44.0  | 44000 | 267.3548        | 0.6847   | 0.5997     | 2.5583 | 0.6847   | 0.6851   | 0.2943 | 0.1769 |
| 263.7822      | 45.0  | 45000 | 267.0005        | 0.682    | 0.6043     | 2.5023 | 0.682    | 0.6793   | 0.2966 | 0.1788 |
| 263.9765      | 46.0  | 46000 | 267.2113        | 0.6853   | 0.5955     | 2.5256 | 0.6853   | 0.6816   | 0.2922 | 0.1737 |
| 264.1576      | 47.0  | 47000 | 267.1731        | 0.6833   | 0.6002     | 2.5071 | 0.6833   | 0.6825   | 0.2951 | 0.1768 |
| 263.8688      | 48.0  | 48000 | 267.0122        | 0.6843   | 0.5980     | 2.5328 | 0.6843   | 0.6830   | 0.2942 | 0.1781 |
| 263.8963      | 49.0  | 49000 | 266.8628        | 0.6843   | 0.6021     | 2.5231 | 0.6843   | 0.6831   | 0.2957 | 0.1782 |
| 264.2061      | 50.0  | 50000 | 267.1454        | 0.6807   | 0.6059     | 2.5092 | 0.6807   | 0.6792   | 0.2988 | 0.1779 |


### Framework versions

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

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