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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_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: 0.0648
- Accuracy: 0.6072
- Brier Loss: 0.5503
- Nll: 2.7228
- F1 Micro: 0.6072
- F1 Macro: 0.6102
- Ece: 0.1175
- Aurc: 0.1871

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0792        | 1.0   | 1000  | 0.0787          | 0.1103   | 0.9330     | 7.5575 | 0.1103   | 0.0531   | 0.0745 | 0.7817 |
| 0.0778        | 2.0   | 2000  | 0.0772          | 0.1867   | 0.9126     | 5.0754 | 0.1867   | 0.1367   | 0.1099 | 0.6583 |
| 0.0753        | 3.0   | 3000  | 0.0748          | 0.3185   | 0.8694     | 3.7575 | 0.3185   | 0.2711   | 0.2000 | 0.4734 |
| 0.0738        | 4.0   | 4000  | 0.0733          | 0.3633   | 0.8431     | 3.5732 | 0.3633   | 0.3239   | 0.2222 | 0.4034 |
| 0.0724        | 5.0   | 5000  | 0.0721          | 0.4083   | 0.8051     | 3.1556 | 0.4083   | 0.3747   | 0.2309 | 0.3543 |
| 0.0712        | 6.0   | 6000  | 0.0716          | 0.422    | 0.7734     | 3.1574 | 0.422    | 0.3887   | 0.1999 | 0.3595 |
| 0.07          | 7.0   | 7000  | 0.0703          | 0.4718   | 0.7584     | 2.9015 | 0.4718   | 0.4495   | 0.2548 | 0.2992 |
| 0.0693        | 8.0   | 8000  | 0.0696          | 0.493    | 0.7247     | 3.0406 | 0.493    | 0.4634   | 0.2430 | 0.2667 |
| 0.0683        | 9.0   | 9000  | 0.0693          | 0.4955   | 0.7122     | 3.1754 | 0.4955   | 0.4725   | 0.2329 | 0.2620 |
| 0.0675        | 10.0  | 10000 | 0.0682          | 0.528    | 0.6893     | 2.7012 | 0.528    | 0.5188   | 0.2477 | 0.2418 |
| 0.0668        | 11.0  | 11000 | 0.0680          | 0.5035   | 0.6961     | 2.9081 | 0.5035   | 0.4967   | 0.2243 | 0.2569 |
| 0.066         | 12.0  | 12000 | 0.0669          | 0.5595   | 0.6626     | 2.6583 | 0.5595   | 0.5541   | 0.2550 | 0.2169 |
| 0.0649        | 13.0  | 13000 | 0.0668          | 0.5493   | 0.6559     | 2.7433 | 0.5493   | 0.5525   | 0.2334 | 0.2165 |
| 0.0645        | 14.0  | 14000 | 0.0663          | 0.5645   | 0.6334     | 2.6699 | 0.5645   | 0.5605   | 0.2191 | 0.2099 |
| 0.0636        | 15.0  | 15000 | 0.0659          | 0.5765   | 0.6160     | 2.6406 | 0.5765   | 0.5664   | 0.2084 | 0.2012 |
| 0.0629        | 16.0  | 16000 | 0.0661          | 0.56     | 0.6311     | 2.7536 | 0.56     | 0.5686   | 0.2129 | 0.2026 |
| 0.0622        | 17.0  | 17000 | 0.0660          | 0.5725   | 0.6108     | 2.8055 | 0.5725   | 0.5707   | 0.1866 | 0.2046 |
| 0.0617        | 18.0  | 18000 | 0.0656          | 0.5697   | 0.6081     | 2.8309 | 0.5697   | 0.5730   | 0.1810 | 0.2024 |
| 0.0608        | 19.0  | 19000 | 0.0654          | 0.585    | 0.5982     | 2.6432 | 0.585    | 0.5867   | 0.1834 | 0.1975 |
| 0.06          | 20.0  | 20000 | 0.0656          | 0.584    | 0.5959     | 2.8363 | 0.584    | 0.5856   | 0.1662 | 0.2067 |
| 0.0592        | 21.0  | 21000 | 0.0657          | 0.5875   | 0.5896     | 2.8259 | 0.5875   | 0.5892   | 0.1575 | 0.2059 |
| 0.0584        | 22.0  | 22000 | 0.0655          | 0.5887   | 0.5832     | 2.8147 | 0.5887   | 0.5895   | 0.1531 | 0.1998 |
| 0.058         | 23.0  | 23000 | 0.0654          | 0.5945   | 0.5829     | 2.9399 | 0.5945   | 0.5955   | 0.1475 | 0.2007 |
| 0.0571        | 24.0  | 24000 | 0.0654          | 0.5962   | 0.5779     | 2.8266 | 0.5962   | 0.5982   | 0.1460 | 0.1996 |
| 0.0566        | 25.0  | 25000 | 0.0655          | 0.596    | 0.5815     | 2.9480 | 0.596    | 0.5975   | 0.1447 | 0.2099 |
| 0.0561        | 26.0  | 26000 | 0.0660          | 0.5883   | 0.5840     | 2.9985 | 0.5883   | 0.5903   | 0.1146 | 0.2202 |
| 0.0556        | 27.0  | 27000 | 0.0654          | 0.6042   | 0.5713     | 2.8775 | 0.6042   | 0.6052   | 0.1353 | 0.2020 |
| 0.055         | 28.0  | 28000 | 0.0655          | 0.5945   | 0.5750     | 3.0404 | 0.5945   | 0.5965   | 0.1215 | 0.2051 |
| 0.0546        | 29.0  | 29000 | 0.0655          | 0.5978   | 0.5740     | 2.9173 | 0.5978   | 0.6012   | 0.1226 | 0.2046 |
| 0.0543        | 30.0  | 30000 | 0.0657          | 0.588    | 0.5813     | 3.0493 | 0.588    | 0.5915   | 0.1210 | 0.2104 |
| 0.054         | 31.0  | 31000 | 0.0652          | 0.597    | 0.5715     | 2.9423 | 0.597    | 0.5989   | 0.1207 | 0.2055 |
| 0.0537        | 32.0  | 32000 | 0.0650          | 0.6075   | 0.5618     | 2.8731 | 0.6075   | 0.6080   | 0.1209 | 0.1987 |
| 0.0534        | 33.0  | 33000 | 0.0650          | 0.602    | 0.5651     | 2.7807 | 0.602    | 0.6046   | 0.1254 | 0.1988 |
| 0.0535        | 34.0  | 34000 | 0.0652          | 0.602    | 0.5661     | 3.0050 | 0.602    | 0.6068   | 0.1187 | 0.1977 |
| 0.053         | 35.0  | 35000 | 0.0649          | 0.6008   | 0.5603     | 2.8814 | 0.6008   | 0.6028   | 0.1172 | 0.1981 |
| 0.0527        | 36.0  | 36000 | 0.0649          | 0.5988   | 0.5575     | 2.8419 | 0.5988   | 0.5974   | 0.1156 | 0.1917 |
| 0.0526        | 37.0  | 37000 | 0.0649          | 0.598    | 0.5586     | 2.7982 | 0.598    | 0.5986   | 0.1173 | 0.1900 |
| 0.0524        | 38.0  | 38000 | 0.0646          | 0.604    | 0.5546     | 2.8202 | 0.604    | 0.6060   | 0.1244 | 0.1908 |
| 0.0524        | 39.0  | 39000 | 0.0651          | 0.5965   | 0.5627     | 2.8458 | 0.5965   | 0.6010   | 0.1125 | 0.1949 |
| 0.0522        | 40.0  | 40000 | 0.0649          | 0.6072   | 0.5515     | 2.7872 | 0.6072   | 0.6100   | 0.1211 | 0.1881 |
| 0.0521        | 41.0  | 41000 | 0.0648          | 0.6078   | 0.5542     | 2.7802 | 0.6078   | 0.6108   | 0.1199 | 0.1868 |
| 0.052         | 42.0  | 42000 | 0.0648          | 0.6      | 0.5557     | 2.7968 | 0.6      | 0.6029   | 0.1190 | 0.1940 |
| 0.0519        | 43.0  | 43000 | 0.0647          | 0.604    | 0.5503     | 2.7110 | 0.604    | 0.6060   | 0.1178 | 0.1896 |
| 0.0516        | 44.0  | 44000 | 0.0647          | 0.6065   | 0.5515     | 2.7595 | 0.6065   | 0.6089   | 0.1170 | 0.1870 |
| 0.0516        | 45.0  | 45000 | 0.0646          | 0.611    | 0.5496     | 2.7426 | 0.611    | 0.6129   | 0.1212 | 0.1873 |
| 0.0515        | 46.0  | 46000 | 0.0648          | 0.6082   | 0.5510     | 2.7436 | 0.6082   | 0.6120   | 0.1227 | 0.1876 |
| 0.0514        | 47.0  | 47000 | 0.0647          | 0.6088   | 0.5511     | 2.7379 | 0.6088   | 0.6115   | 0.1240 | 0.1874 |
| 0.0514        | 48.0  | 48000 | 0.0647          | 0.6095   | 0.5501     | 2.7369 | 0.6095   | 0.6122   | 0.1193 | 0.1868 |
| 0.0513        | 49.0  | 49000 | 0.0647          | 0.6095   | 0.5508     | 2.7295 | 0.6095   | 0.6122   | 0.1218 | 0.1870 |
| 0.0513        | 50.0  | 50000 | 0.0648          | 0.6072   | 0.5503     | 2.7228 | 0.6072   | 0.6102   | 0.1175 | 0.1871 |


### Framework versions

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

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