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# vit-base_rvl-cdip-tiny_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: 267.6730
- Accuracy: 0.6705
- Brier Loss: 0.6262
- Nll: 2.7104
- F1 Micro: 0.6705
- F1 Macro: 0.6721
- Ece: 0.3087
- Aurc: 0.1976

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 286.7271      | 1.0   | 1000  | 285.5399        | 0.2112   | 1.1285     | 5.2382 | 0.2112   | 0.1362   | 0.4400 | 0.6668 |
| 284.6535      | 2.0   | 2000  | 284.8639        | 0.2365   | 1.1876     | 6.1414 | 0.2365   | 0.1846   | 0.5026 | 0.6043 |
| 283.982       | 3.0   | 3000  | 284.8751        | 0.2555   | 1.2913     | 6.7626 | 0.2555   | 0.2072   | 0.5840 | 0.6111 |
| 283.8947      | 4.0   | 4000  | 283.0353        | 0.3585   | 1.0748     | 4.2918 | 0.3585   | 0.3100   | 0.4921 | 0.4239 |
| 282.5615      | 5.0   | 5000  | 282.0369        | 0.3852   | 1.0142     | 4.7413 | 0.3852   | 0.3432   | 0.4558 | 0.3983 |
| 281.6467      | 6.0   | 6000  | 280.8857        | 0.428    | 0.9539     | 4.1971 | 0.428    | 0.3797   | 0.4329 | 0.3427 |
| 280.8835      | 7.0   | 7000  | 279.7836        | 0.4288   | 1.0391     | 3.9288 | 0.4288   | 0.4012   | 0.4994 | 0.3565 |
| 279.5518      | 8.0   | 8000  | 278.7849        | 0.5198   | 0.8045     | 3.0811 | 0.5198   | 0.4977   | 0.3699 | 0.2454 |
| 278.6091      | 9.0   | 9000  | 278.3536        | 0.5155   | 0.8487     | 3.1204 | 0.5155   | 0.4977   | 0.4004 | 0.2587 |
| 277.9435      | 10.0  | 10000 | 277.6002        | 0.5258   | 0.8346     | 3.3232 | 0.5258   | 0.4899   | 0.3923 | 0.2693 |
| 277.646       | 11.0  | 11000 | 276.9034        | 0.5285   | 0.8510     | 3.1019 | 0.5285   | 0.5010   | 0.4079 | 0.2804 |
| 276.6211      | 12.0  | 12000 | 276.8536        | 0.5555   | 0.7899     | 3.0560 | 0.5555   | 0.5446   | 0.3760 | 0.2266 |
| 276.1643      | 13.0  | 13000 | 275.8300        | 0.5685   | 0.7767     | 3.1275 | 0.5685   | 0.5412   | 0.3730 | 0.2267 |
| 275.7773      | 14.0  | 14000 | 275.0154        | 0.5833   | 0.7536     | 2.9981 | 0.5833   | 0.5645   | 0.3603 | 0.2357 |
| 274.971       | 15.0  | 15000 | 275.1284        | 0.6008   | 0.7210     | 2.8953 | 0.6008   | 0.5920   | 0.3414 | 0.2059 |
| 274.6605      | 16.0  | 16000 | 273.9564        | 0.6132   | 0.7168     | 2.8476 | 0.6132   | 0.5968   | 0.3479 | 0.2272 |
| 273.7713      | 17.0  | 17000 | 273.3493        | 0.5995   | 0.7409     | 2.8991 | 0.5995   | 0.5901   | 0.3607 | 0.2272 |
| 272.7905      | 18.0  | 18000 | 273.5748        | 0.598    | 0.7367     | 2.7778 | 0.598    | 0.5858   | 0.3565 | 0.2102 |
| 273.134       | 19.0  | 19000 | 272.6561        | 0.6158   | 0.7128     | 2.8084 | 0.6158   | 0.6023   | 0.3494 | 0.2132 |
| 271.8558      | 20.0  | 20000 | 272.4530        | 0.618    | 0.7139     | 2.9767 | 0.618    | 0.6077   | 0.3480 | 0.2177 |
| 271.9448      | 21.0  | 21000 | 272.1698        | 0.619    | 0.7164     | 2.9459 | 0.619    | 0.6133   | 0.3510 | 0.2256 |
| 270.9343      | 22.0  | 22000 | 272.2906        | 0.6235   | 0.7087     | 2.9843 | 0.6235   | 0.6181   | 0.3452 | 0.2248 |
| 270.6012      | 23.0  | 23000 | 271.5266        | 0.6382   | 0.6781     | 2.9158 | 0.6382   | 0.6352   | 0.3324 | 0.2110 |
| 270.3184      | 24.0  | 24000 | 271.1095        | 0.634    | 0.6922     | 2.9734 | 0.634    | 0.6287   | 0.3348 | 0.2162 |
| 269.5019      | 25.0  | 25000 | 270.8806        | 0.644    | 0.6683     | 2.8735 | 0.644    | 0.6359   | 0.3258 | 0.2123 |
| 269.5113      | 26.0  | 26000 | 270.6180        | 0.6445   | 0.6650     | 2.6933 | 0.6445   | 0.6418   | 0.3271 | 0.2032 |
| 269.1238      | 27.0  | 27000 | 270.1308        | 0.6445   | 0.6712     | 2.8097 | 0.6445   | 0.6462   | 0.3290 | 0.2128 |
| 268.424       | 28.0  | 28000 | 269.7667        | 0.6352   | 0.6872     | 2.9166 | 0.6352   | 0.6314   | 0.3371 | 0.2231 |
| 268.4034      | 29.0  | 29000 | 270.0039        | 0.6455   | 0.6685     | 2.7765 | 0.6455   | 0.6459   | 0.3273 | 0.2097 |
| 268.3632      | 30.0  | 30000 | 270.0340        | 0.6448   | 0.6741     | 2.8602 | 0.6448   | 0.6455   | 0.3291 | 0.2178 |
| 268.1831      | 31.0  | 31000 | 269.3010        | 0.6597   | 0.6467     | 2.7502 | 0.6597   | 0.6571   | 0.3176 | 0.2053 |
| 268.0006      | 32.0  | 32000 | 269.4335        | 0.652    | 0.6583     | 2.8213 | 0.652    | 0.6457   | 0.3236 | 0.2081 |
| 267.5016      | 33.0  | 33000 | 269.2711        | 0.654    | 0.6530     | 2.8720 | 0.654    | 0.6517   | 0.3199 | 0.2090 |
| 267.177       | 34.0  | 34000 | 268.7774        | 0.661    | 0.6402     | 2.7718 | 0.661    | 0.6589   | 0.3137 | 0.1979 |
| 266.8408      | 35.0  | 35000 | 268.8279        | 0.6478   | 0.6640     | 2.8626 | 0.6478   | 0.6472   | 0.3271 | 0.2204 |
| 266.1984      | 36.0  | 36000 | 268.3442        | 0.6635   | 0.6378     | 2.7999 | 0.6635   | 0.6611   | 0.3128 | 0.2079 |
| 266.1338      | 37.0  | 37000 | 268.5704        | 0.66     | 0.6430     | 2.8314 | 0.66     | 0.6576   | 0.3165 | 0.2039 |
| 266.6958      | 38.0  | 38000 | 268.1453        | 0.6635   | 0.6415     | 2.7881 | 0.6635   | 0.6627   | 0.3147 | 0.2106 |
| 265.6171      | 39.0  | 39000 | 268.1818        | 0.6635   | 0.6398     | 2.7602 | 0.6635   | 0.6641   | 0.3142 | 0.2025 |
| 265.8238      | 40.0  | 40000 | 268.1265        | 0.6637   | 0.6390     | 2.8178 | 0.6637   | 0.6648   | 0.3151 | 0.2016 |
| 265.4164      | 41.0  | 41000 | 267.8777        | 0.6663   | 0.6304     | 2.7649 | 0.6663   | 0.6664   | 0.3113 | 0.2012 |
| 265.6293      | 42.0  | 42000 | 267.8370        | 0.6683   | 0.6285     | 2.7730 | 0.6683   | 0.6677   | 0.3108 | 0.2023 |
| 265.6068      | 43.0  | 43000 | 267.7586        | 0.665    | 0.6348     | 2.7612 | 0.665    | 0.6649   | 0.3126 | 0.1992 |
| 265.2131      | 44.0  | 44000 | 268.0432        | 0.667    | 0.6293     | 2.7217 | 0.667    | 0.6669   | 0.3094 | 0.1885 |
| 265.1312      | 45.0  | 45000 | 267.6967        | 0.6653   | 0.6316     | 2.6899 | 0.6653   | 0.6637   | 0.3127 | 0.2000 |
| 265.371       | 46.0  | 46000 | 267.5307        | 0.668    | 0.6317     | 2.7472 | 0.668    | 0.6684   | 0.3105 | 0.2000 |
| 264.9213      | 47.0  | 47000 | 267.5887        | 0.672    | 0.6214     | 2.6635 | 0.672    | 0.6720   | 0.3063 | 0.1935 |
| 265.1304      | 48.0  | 48000 | 267.4995        | 0.6735   | 0.6220     | 2.7437 | 0.6735   | 0.6730   | 0.3049 | 0.1958 |
| 264.6242      | 49.0  | 49000 | 267.2600        | 0.6723   | 0.6236     | 2.8222 | 0.6723   | 0.6713   | 0.3074 | 0.1974 |
| 265.1563      | 50.0  | 50000 | 267.6730        | 0.6705   | 0.6262     | 2.7104 | 0.6705   | 0.6721   | 0.3087 | 0.1976 |


### Framework versions

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

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