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# vit-base_rvl-cdip-tiny_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: 0.0667
- Accuracy: 0.5865
- Brier Loss: 0.5908
- Nll: 3.0393
- F1 Micro: 0.5865
- F1 Macro: 0.5890
- Ece: 0.1479
- Aurc: 0.2054

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0807        | 1.0   | 1000  | 0.0798          | 0.095    | 0.9362     | 7.0778 | 0.095    | 0.0517   | 0.0524 | 0.8510 |
| 0.0785        | 2.0   | 2000  | 0.0782          | 0.142    | 0.9268     | 6.5000 | 0.142    | 0.0892   | 0.0843 | 0.7446 |
| 0.0768        | 3.0   | 3000  | 0.0761          | 0.253    | 0.8945     | 4.3268 | 0.253    | 0.1827   | 0.1545 | 0.5697 |
| 0.0753        | 4.0   | 4000  | 0.0747          | 0.327    | 0.8672     | 3.7313 | 0.327    | 0.2733   | 0.2052 | 0.4558 |
| 0.074         | 5.0   | 5000  | 0.0739          | 0.359    | 0.8410     | 3.6965 | 0.359    | 0.2941   | 0.2102 | 0.4159 |
| 0.0729        | 6.0   | 6000  | 0.0725          | 0.3795   | 0.8104     | 3.2323 | 0.3795   | 0.3340   | 0.2147 | 0.3672 |
| 0.0718        | 7.0   | 7000  | 0.0717          | 0.4165   | 0.7806     | 3.1185 | 0.4165   | 0.3770   | 0.2186 | 0.3378 |
| 0.071         | 8.0   | 8000  | 0.0714          | 0.4175   | 0.7785     | 3.1984 | 0.4175   | 0.3999   | 0.2170 | 0.3408 |
| 0.0703        | 9.0   | 9000  | 0.0707          | 0.457    | 0.7563     | 2.8932 | 0.457    | 0.4310   | 0.2437 | 0.2965 |
| 0.0696        | 10.0  | 10000 | 0.0699          | 0.4665   | 0.7452     | 2.7889 | 0.4665   | 0.4529   | 0.2456 | 0.2828 |
| 0.0691        | 11.0  | 11000 | 0.0693          | 0.499    | 0.7219     | 2.7292 | 0.499    | 0.4756   | 0.2543 | 0.2579 |
| 0.0685        | 12.0  | 12000 | 0.0691          | 0.4955   | 0.7144     | 2.8807 | 0.4955   | 0.4734   | 0.2443 | 0.2515 |
| 0.068         | 13.0  | 13000 | 0.0688          | 0.5072   | 0.7096     | 2.6737 | 0.5072   | 0.4944   | 0.2525 | 0.2468 |
| 0.0675        | 14.0  | 14000 | 0.0685          | 0.513    | 0.6952     | 2.7492 | 0.513    | 0.5001   | 0.2404 | 0.2453 |
| 0.0669        | 15.0  | 15000 | 0.0682          | 0.5232   | 0.6855     | 2.7789 | 0.5232   | 0.5048   | 0.2441 | 0.2379 |
| 0.0664        | 16.0  | 16000 | 0.0680          | 0.529    | 0.6790     | 2.8249 | 0.529    | 0.5182   | 0.2366 | 0.2340 |
| 0.0658        | 17.0  | 17000 | 0.0678          | 0.5347   | 0.6668     | 2.7035 | 0.5347   | 0.5237   | 0.2338 | 0.2228 |
| 0.0652        | 18.0  | 18000 | 0.0676          | 0.5335   | 0.6673     | 2.8630 | 0.5335   | 0.5249   | 0.2319 | 0.2252 |
| 0.0651        | 19.0  | 19000 | 0.0675          | 0.5385   | 0.6524     | 2.7522 | 0.5385   | 0.5286   | 0.2172 | 0.2256 |
| 0.0645        | 20.0  | 20000 | 0.0671          | 0.5593   | 0.6454     | 2.7445 | 0.5593   | 0.5563   | 0.2324 | 0.2122 |
| 0.0639        | 21.0  | 21000 | 0.0672          | 0.5453   | 0.6541     | 2.9011 | 0.5453   | 0.5451   | 0.2236 | 0.2204 |
| 0.0634        | 22.0  | 22000 | 0.0668          | 0.5617   | 0.6398     | 2.8668 | 0.5617   | 0.5604   | 0.2264 | 0.2108 |
| 0.0629        | 23.0  | 23000 | 0.0670          | 0.5577   | 0.6295     | 2.8351 | 0.5577   | 0.5521   | 0.1984 | 0.2180 |
| 0.0625        | 24.0  | 24000 | 0.0666          | 0.5765   | 0.6201     | 2.7133 | 0.5765   | 0.5754   | 0.2138 | 0.2035 |
| 0.0618        | 25.0  | 25000 | 0.0666          | 0.565    | 0.6219     | 2.8775 | 0.565    | 0.5614   | 0.2010 | 0.2078 |
| 0.0613        | 26.0  | 26000 | 0.0664          | 0.5795   | 0.6121     | 2.8665 | 0.5795   | 0.5805   | 0.1996 | 0.2024 |
| 0.0606        | 27.0  | 27000 | 0.0667          | 0.5723   | 0.6101     | 2.9450 | 0.5723   | 0.5711   | 0.1804 | 0.2113 |
| 0.0603        | 28.0  | 28000 | 0.0664          | 0.583    | 0.6106     | 2.9126 | 0.583    | 0.5845   | 0.2004 | 0.2006 |
| 0.0597        | 29.0  | 29000 | 0.0665          | 0.5857   | 0.6050     | 2.9881 | 0.5857   | 0.5862   | 0.1912 | 0.2006 |
| 0.0594        | 30.0  | 30000 | 0.0665          | 0.5775   | 0.6043     | 2.9735 | 0.5775   | 0.5797   | 0.1823 | 0.2029 |
| 0.0589        | 31.0  | 31000 | 0.0666          | 0.5733   | 0.6080     | 2.9942 | 0.5733   | 0.5739   | 0.1721 | 0.2129 |
| 0.0585        | 32.0  | 32000 | 0.0667          | 0.5803   | 0.6066     | 3.0341 | 0.5803   | 0.5826   | 0.1748 | 0.2114 |
| 0.0583        | 33.0  | 33000 | 0.0665          | 0.5827   | 0.6033     | 3.0209 | 0.5827   | 0.5880   | 0.1799 | 0.2029 |
| 0.0578        | 34.0  | 34000 | 0.0667          | 0.577    | 0.6020     | 3.0483 | 0.577    | 0.5816   | 0.1636 | 0.2081 |
| 0.0576        | 35.0  | 35000 | 0.0667          | 0.577    | 0.6029     | 3.0263 | 0.577    | 0.5840   | 0.1573 | 0.2117 |
| 0.0574        | 36.0  | 36000 | 0.0667          | 0.5803   | 0.6006     | 3.0578 | 0.5803   | 0.5851   | 0.1627 | 0.2082 |
| 0.057         | 37.0  | 37000 | 0.0666          | 0.582    | 0.5997     | 3.1133 | 0.582    | 0.5867   | 0.1612 | 0.2094 |
| 0.0567        | 38.0  | 38000 | 0.0667          | 0.5817   | 0.5951     | 3.0727 | 0.5817   | 0.5836   | 0.1552 | 0.2091 |
| 0.0566        | 39.0  | 39000 | 0.0666          | 0.5815   | 0.5951     | 3.0308 | 0.5815   | 0.5853   | 0.1559 | 0.2049 |
| 0.0564        | 40.0  | 40000 | 0.0666          | 0.5853   | 0.5940     | 3.0629 | 0.5853   | 0.5880   | 0.1564 | 0.2057 |
| 0.0562        | 41.0  | 41000 | 0.0666          | 0.5845   | 0.5949     | 3.0956 | 0.5845   | 0.5881   | 0.1585 | 0.2055 |
| 0.0561        | 42.0  | 42000 | 0.0666          | 0.5827   | 0.5960     | 3.0679 | 0.5827   | 0.5876   | 0.1540 | 0.2098 |
| 0.0559        | 43.0  | 43000 | 0.0666          | 0.5833   | 0.5909     | 2.9904 | 0.5833   | 0.5854   | 0.1491 | 0.2049 |
| 0.0559        | 44.0  | 44000 | 0.0665          | 0.585    | 0.5915     | 3.0150 | 0.585    | 0.5876   | 0.1543 | 0.2032 |
| 0.0557        | 45.0  | 45000 | 0.0667          | 0.583    | 0.5923     | 3.0501 | 0.583    | 0.5851   | 0.1501 | 0.2056 |
| 0.0557        | 46.0  | 46000 | 0.0666          | 0.5905   | 0.5914     | 3.0110 | 0.5905   | 0.5940   | 0.1550 | 0.2045 |
| 0.0555        | 47.0  | 47000 | 0.0667          | 0.584    | 0.5922     | 3.0464 | 0.584    | 0.5872   | 0.1497 | 0.2069 |
| 0.0555        | 48.0  | 48000 | 0.0667          | 0.588    | 0.5917     | 3.0408 | 0.588    | 0.5919   | 0.1489 | 0.2051 |
| 0.0554        | 49.0  | 49000 | 0.0667          | 0.589    | 0.5908     | 3.0433 | 0.589    | 0.5923   | 0.1496 | 0.2044 |
| 0.0554        | 50.0  | 50000 | 0.0667          | 0.5865   | 0.5908     | 3.0393 | 0.5865   | 0.5890   | 0.1479 | 0.2054 |


### Framework versions

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

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