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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_hint_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: 74.2895
- Accuracy: 0.6627
- Brier Loss: 0.6224
- Nll: 3.3689
- F1 Micro: 0.6627
- F1 Macro: 0.6637
- Ece: 0.3019
- Aurc: 0.1471

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 77.1398       | 1.0   | 1000  | 76.8452         | 0.1923   | 0.8714     | 4.4881 | 0.1923   | 0.1046   | 0.0814 | 0.6378 |
| 75.9619       | 2.0   | 2000  | 75.9373         | 0.3513   | 0.7709     | 3.0790 | 0.3513   | 0.3110   | 0.0596 | 0.4537 |
| 75.5047       | 3.0   | 3000  | 75.7291         | 0.4233   | 0.7112     | 3.0280 | 0.4233   | 0.3913   | 0.0610 | 0.3648 |
| 75.4727       | 4.0   | 4000  | 75.5639         | 0.4163   | 0.7147     | 3.0030 | 0.4163   | 0.3863   | 0.0669 | 0.3662 |
| 75.146        | 5.0   | 5000  | 75.4176         | 0.467    | 0.6695     | 2.8545 | 0.467    | 0.4530   | 0.0563 | 0.3180 |
| 74.8201       | 6.0   | 6000  | 74.8222         | 0.5275   | 0.6023     | 2.6409 | 0.5275   | 0.5201   | 0.0587 | 0.2448 |
| 74.4727       | 7.0   | 7000  | 74.6341         | 0.5403   | 0.5930     | 2.5700 | 0.5403   | 0.5312   | 0.0707 | 0.2393 |
| 74.1392       | 8.0   | 8000  | 74.6029         | 0.5615   | 0.5669     | 2.5716 | 0.5615   | 0.5496   | 0.0666 | 0.2142 |
| 74.165        | 9.0   | 9000  | 74.4072         | 0.5863   | 0.5479     | 2.5087 | 0.5863   | 0.5793   | 0.0689 | 0.1969 |
| 73.8821       | 10.0  | 10000 | 74.2595         | 0.5817   | 0.5517     | 2.4910 | 0.5817   | 0.5802   | 0.0733 | 0.1973 |
| 73.6199       | 11.0  | 11000 | 74.2044         | 0.61     | 0.5233     | 2.4183 | 0.61     | 0.6001   | 0.0853 | 0.1722 |
| 73.4772       | 12.0  | 12000 | 73.9341         | 0.593    | 0.5520     | 2.4592 | 0.593    | 0.5873   | 0.1244 | 0.1847 |
| 73.2445       | 13.0  | 13000 | 73.9870         | 0.614    | 0.5368     | 2.5577 | 0.614    | 0.6093   | 0.1303 | 0.1706 |
| 73.1468       | 14.0  | 14000 | 73.9027         | 0.6212   | 0.5368     | 2.6082 | 0.6212   | 0.6192   | 0.1340 | 0.1691 |
| 72.9154       | 15.0  | 15000 | 73.7745         | 0.6298   | 0.5353     | 2.5866 | 0.6298   | 0.6260   | 0.1564 | 0.1598 |
| 72.7416       | 16.0  | 16000 | 73.8946         | 0.6225   | 0.5528     | 2.5893 | 0.6225   | 0.6245   | 0.1810 | 0.1679 |
| 72.4708       | 17.0  | 17000 | 73.9445         | 0.62     | 0.5757     | 2.7436 | 0.62     | 0.6217   | 0.2044 | 0.1714 |
| 72.5169       | 18.0  | 18000 | 73.7757         | 0.6262   | 0.5741     | 2.6894 | 0.6262   | 0.6292   | 0.2139 | 0.1655 |
| 72.2021       | 19.0  | 19000 | 73.9482         | 0.6192   | 0.6063     | 2.8813 | 0.6192   | 0.6175   | 0.2534 | 0.1706 |
| 72.1296       | 20.0  | 20000 | 73.9725         | 0.6185   | 0.6135     | 2.9223 | 0.6185   | 0.6223   | 0.2495 | 0.1736 |
| 72.1903       | 21.0  | 21000 | 74.0277         | 0.6285   | 0.6091     | 2.8760 | 0.6285   | 0.6307   | 0.2588 | 0.1638 |
| 71.9868       | 22.0  | 22000 | 74.1811         | 0.6218   | 0.6317     | 3.0858 | 0.6218   | 0.6229   | 0.2792 | 0.1698 |
| 71.9677       | 23.0  | 23000 | 74.1227         | 0.6222   | 0.6442     | 3.0329 | 0.6222   | 0.6214   | 0.2872 | 0.1764 |
| 71.8254       | 24.0  | 24000 | 74.2927         | 0.6282   | 0.6412     | 3.1773 | 0.6282   | 0.6220   | 0.2928 | 0.1739 |
| 71.7948       | 25.0  | 25000 | 74.1580         | 0.626    | 0.6498     | 3.1230 | 0.626    | 0.6286   | 0.3007 | 0.1703 |
| 71.6915       | 26.0  | 26000 | 74.1776         | 0.6335   | 0.6367     | 3.1272 | 0.6335   | 0.6351   | 0.2937 | 0.1655 |
| 71.4526       | 27.0  | 27000 | 74.4076         | 0.6335   | 0.6519     | 3.3331 | 0.6335   | 0.6318   | 0.3023 | 0.1749 |
| 71.2967       | 28.0  | 28000 | 74.1954         | 0.6402   | 0.6361     | 3.1669 | 0.6402   | 0.6392   | 0.2995 | 0.1618 |
| 71.4139       | 29.0  | 29000 | 74.2737         | 0.6342   | 0.6454     | 3.0744 | 0.6342   | 0.6347   | 0.3070 | 0.1626 |
| 71.3204       | 30.0  | 30000 | 74.2779         | 0.652    | 0.6277     | 3.2286 | 0.652    | 0.6550   | 0.2956 | 0.1572 |
| 71.4168       | 31.0  | 31000 | 74.3630         | 0.6458   | 0.6386     | 3.2327 | 0.6458   | 0.6463   | 0.3032 | 0.1594 |
| 71.387        | 32.0  | 32000 | 74.4710         | 0.6522   | 0.6383     | 3.3193 | 0.6522   | 0.6526   | 0.3016 | 0.1610 |
| 71.2382       | 33.0  | 33000 | 74.4096         | 0.652    | 0.6275     | 3.3440 | 0.652    | 0.6522   | 0.2977 | 0.1584 |
| 71.1387       | 34.0  | 34000 | 74.2451         | 0.6512   | 0.6316     | 3.2834 | 0.6512   | 0.6525   | 0.3022 | 0.1555 |
| 71.0904       | 35.0  | 35000 | 74.2640         | 0.6525   | 0.6341     | 3.1942 | 0.6525   | 0.6518   | 0.3023 | 0.1521 |
| 70.9615       | 36.0  | 36000 | 74.1828         | 0.6565   | 0.6239     | 3.1805 | 0.6565   | 0.6568   | 0.3014 | 0.1516 |
| 71.0673       | 37.0  | 37000 | 74.3405         | 0.6498   | 0.6341     | 3.3365 | 0.6498   | 0.6518   | 0.3071 | 0.1556 |
| 71.0009       | 38.0  | 38000 | 74.2596         | 0.6595   | 0.6296     | 3.3359 | 0.6595   | 0.6622   | 0.2991 | 0.1512 |
| 70.8441       | 39.0  | 39000 | 74.2837         | 0.6593   | 0.6254     | 3.3852 | 0.6593   | 0.6609   | 0.3005 | 0.1537 |
| 70.8273       | 40.0  | 40000 | 74.3321         | 0.6567   | 0.6342     | 3.3111 | 0.6567   | 0.6589   | 0.3068 | 0.1544 |
| 70.8931       | 41.0  | 41000 | 74.3478         | 0.662    | 0.6253     | 3.3022 | 0.662    | 0.6604   | 0.3029 | 0.1474 |
| 70.8954       | 42.0  | 42000 | 74.2638         | 0.6613   | 0.6275     | 3.3811 | 0.6613   | 0.6612   | 0.3033 | 0.1499 |
| 70.7389       | 43.0  | 43000 | 74.2531         | 0.6633   | 0.6221     | 3.3627 | 0.6633   | 0.6650   | 0.2998 | 0.1489 |
| 70.7911       | 44.0  | 44000 | 74.3263         | 0.6587   | 0.6299     | 3.3918 | 0.6587   | 0.6588   | 0.3037 | 0.1496 |
| 70.8719       | 45.0  | 45000 | 74.2778         | 0.6627   | 0.6236     | 3.3826 | 0.6627   | 0.6641   | 0.3009 | 0.1480 |
| 70.7289       | 46.0  | 46000 | 74.2760         | 0.6625   | 0.6201     | 3.3467 | 0.6625   | 0.6635   | 0.3016 | 0.1469 |
| 70.8773       | 47.0  | 47000 | 74.2709         | 0.6643   | 0.6185     | 3.3370 | 0.6643   | 0.6660   | 0.2989 | 0.1476 |
| 70.6951       | 48.0  | 48000 | 74.2857         | 0.6643   | 0.6218     | 3.3545 | 0.6643   | 0.6648   | 0.2995 | 0.1477 |
| 70.8059       | 49.0  | 49000 | 74.3124         | 0.6623   | 0.6228     | 3.3592 | 0.6623   | 0.6634   | 0.3020 | 0.1470 |
| 70.6955       | 50.0  | 50000 | 74.2895         | 0.6627   | 0.6224     | 3.3689 | 0.6627   | 0.6637   | 0.3019 | 0.1471 |


### Framework versions

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

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