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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_hint

This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 72.4419
- Accuracy: 0.8455
- Brier Loss: 0.2823
- Nll: 1.9713
- F1 Micro: 0.8455
- F1 Macro: 0.8458
- Ece: 0.1379
- Aurc: 0.0501

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 75.292        | 1.0   | 1000  | 74.9267         | 0.669    | 0.4449     | 2.2033 | 0.669    | 0.6688   | 0.0509 | 0.1261 |
| 74.3102       | 2.0   | 2000  | 74.4753         | 0.7228   | 0.3815     | 2.1695 | 0.7228   | 0.7241   | 0.0443 | 0.0931 |
| 73.8711       | 3.0   | 3000  | 74.1051         | 0.7718   | 0.3245     | 1.9516 | 0.7717   | 0.7726   | 0.0520 | 0.0687 |
| 73.8038       | 4.0   | 4000  | 74.0142         | 0.764    | 0.3412     | 1.9168 | 0.764    | 0.7656   | 0.0862 | 0.0705 |
| 73.5111       | 5.0   | 5000  | 73.9154         | 0.762    | 0.3443     | 1.9320 | 0.762    | 0.7684   | 0.0774 | 0.0734 |
| 73.2405       | 6.0   | 6000  | 73.6544         | 0.7923   | 0.3034     | 1.9497 | 0.7923   | 0.7877   | 0.0780 | 0.0555 |
| 72.892        | 7.0   | 7000  | 73.6057         | 0.8073   | 0.3042     | 1.9087 | 0.8073   | 0.8074   | 0.1072 | 0.0549 |
| 72.6235       | 8.0   | 8000  | 73.6740         | 0.8007   | 0.3175     | 1.9674 | 0.8007   | 0.8002   | 0.1221 | 0.0566 |
| 72.6609       | 9.0   | 9000  | 73.6103         | 0.7977   | 0.3290     | 2.0128 | 0.7977   | 0.7992   | 0.1345 | 0.0595 |
| 72.4643       | 10.0  | 10000 | 73.5996         | 0.8143   | 0.3166     | 1.9412 | 0.8143   | 0.8148   | 0.1372 | 0.0498 |
| 72.2435       | 11.0  | 11000 | 73.5005         | 0.8057   | 0.3268     | 1.9323 | 0.8057   | 0.8070   | 0.1469 | 0.0530 |
| 72.1757       | 12.0  | 12000 | 73.5284         | 0.8165   | 0.3197     | 1.9353 | 0.8165   | 0.8175   | 0.1442 | 0.0511 |
| 72.0746       | 13.0  | 13000 | 73.5250         | 0.8023   | 0.3434     | 2.0172 | 0.8023   | 0.8047   | 0.1577 | 0.0584 |
| 72.0251       | 14.0  | 14000 | 73.3937         | 0.817    | 0.3176     | 1.9784 | 0.817    | 0.8172   | 0.1471 | 0.0510 |
| 71.8588       | 15.0  | 15000 | 73.3792         | 0.814    | 0.3249     | 1.9812 | 0.8140   | 0.8148   | 0.1506 | 0.0514 |
| 71.8093       | 16.0  | 16000 | 73.2188         | 0.825    | 0.3084     | 1.9155 | 0.825    | 0.8259   | 0.1446 | 0.0449 |
| 71.5835       | 17.0  | 17000 | 73.2452         | 0.8307   | 0.3019     | 1.9447 | 0.8308   | 0.8308   | 0.1386 | 0.0437 |
| 71.6995       | 18.0  | 18000 | 73.2568         | 0.83     | 0.3042     | 2.0599 | 0.83     | 0.8304   | 0.1444 | 0.0459 |
| 71.4455       | 19.0  | 19000 | 73.2655         | 0.8203   | 0.3204     | 2.0173 | 0.8203   | 0.8205   | 0.1524 | 0.0484 |
| 71.4047       | 20.0  | 20000 | 73.1947         | 0.8203   | 0.3218     | 1.9651 | 0.8203   | 0.8218   | 0.1524 | 0.0484 |
| 71.5116       | 21.0  | 21000 | 73.1398         | 0.8217   | 0.3197     | 1.9803 | 0.8217   | 0.8209   | 0.1521 | 0.0453 |
| 71.3315       | 22.0  | 22000 | 73.0498         | 0.832    | 0.3033     | 1.9617 | 0.832    | 0.8327   | 0.1445 | 0.0478 |
| 71.329        | 23.0  | 23000 | 73.0645         | 0.8277   | 0.3129     | 1.9073 | 0.8277   | 0.8293   | 0.1502 | 0.0486 |
| 71.2208       | 24.0  | 24000 | 73.1448         | 0.8257   | 0.3188     | 2.0056 | 0.8257   | 0.8252   | 0.1511 | 0.0520 |
| 71.22         | 25.0  | 25000 | 72.9177         | 0.827    | 0.3103     | 1.9543 | 0.827    | 0.8261   | 0.1493 | 0.0500 |
| 71.1268       | 26.0  | 26000 | 72.9064         | 0.8323   | 0.3022     | 1.9069 | 0.8323   | 0.8311   | 0.1458 | 0.0463 |
| 70.8954       | 27.0  | 27000 | 72.8821         | 0.8403   | 0.2930     | 1.9264 | 0.8403   | 0.8405   | 0.1399 | 0.0454 |
| 70.7553       | 28.0  | 28000 | 72.8230         | 0.8347   | 0.3012     | 1.9356 | 0.8347   | 0.8347   | 0.1434 | 0.0487 |
| 70.8785       | 29.0  | 29000 | 72.8549         | 0.8347   | 0.3023     | 1.9905 | 0.8347   | 0.8339   | 0.1464 | 0.0477 |
| 70.8139       | 30.0  | 30000 | 72.8073         | 0.8405   | 0.2933     | 1.9148 | 0.8405   | 0.8400   | 0.1399 | 0.0482 |
| 70.9162       | 31.0  | 31000 | 72.7751         | 0.8367   | 0.3005     | 1.9466 | 0.8367   | 0.8364   | 0.1441 | 0.0473 |
| 70.8988       | 32.0  | 32000 | 72.7235         | 0.8365   | 0.2990     | 1.9178 | 0.8365   | 0.8362   | 0.1432 | 0.0453 |
| 70.7529       | 33.0  | 33000 | 72.6744         | 0.8415   | 0.2937     | 1.9929 | 0.8415   | 0.8424   | 0.1391 | 0.0491 |
| 70.6705       | 34.0  | 34000 | 72.6624         | 0.8407   | 0.2927     | 1.9562 | 0.8407   | 0.8423   | 0.1417 | 0.0490 |
| 70.6404       | 35.0  | 35000 | 72.7689         | 0.8317   | 0.3071     | 2.0079 | 0.8317   | 0.8311   | 0.1479 | 0.0551 |
| 70.5201       | 36.0  | 36000 | 72.6579         | 0.8425   | 0.2928     | 1.9879 | 0.8425   | 0.8431   | 0.1404 | 0.0519 |
| 70.6383       | 37.0  | 37000 | 72.5850         | 0.8458   | 0.2839     | 1.9513 | 0.8458   | 0.8470   | 0.1362 | 0.0500 |
| 70.5781       | 38.0  | 38000 | 72.5590         | 0.8423   | 0.2894     | 1.9416 | 0.8423   | 0.8427   | 0.1407 | 0.0496 |
| 70.4386       | 39.0  | 39000 | 72.5131         | 0.8435   | 0.2855     | 1.9475 | 0.8435   | 0.8443   | 0.1397 | 0.0492 |
| 70.4275       | 40.0  | 40000 | 72.5441         | 0.8455   | 0.2896     | 1.9216 | 0.8455   | 0.8460   | 0.1387 | 0.0481 |
| 70.5018       | 41.0  | 41000 | 72.5071         | 0.8452   | 0.2866     | 1.9148 | 0.8452   | 0.8465   | 0.1384 | 0.0486 |
| 70.5176       | 42.0  | 42000 | 72.5110         | 0.8445   | 0.2862     | 1.9591 | 0.8445   | 0.8447   | 0.1384 | 0.0523 |
| 70.3675       | 43.0  | 43000 | 72.4968         | 0.844    | 0.2845     | 1.9376 | 0.844    | 0.8445   | 0.1398 | 0.0507 |
| 70.4295       | 44.0  | 44000 | 72.4633         | 0.846    | 0.2838     | 1.9475 | 0.8460   | 0.8464   | 0.1381 | 0.0506 |
| 70.5198       | 45.0  | 45000 | 72.4940         | 0.8468   | 0.2828     | 1.9559 | 0.8468   | 0.8471   | 0.1374 | 0.0501 |
| 70.3838       | 46.0  | 46000 | 72.4658         | 0.8462   | 0.2823     | 1.9468 | 0.8462   | 0.8465   | 0.1366 | 0.0500 |
| 70.5383       | 47.0  | 47000 | 72.4590         | 0.8475   | 0.2817     | 1.9537 | 0.8475   | 0.8480   | 0.1362 | 0.0502 |
| 70.3619       | 48.0  | 48000 | 72.4522         | 0.8458   | 0.2824     | 1.9632 | 0.8458   | 0.8461   | 0.1377 | 0.0495 |
| 70.4764       | 49.0  | 49000 | 72.4573         | 0.8455   | 0.2825     | 1.9689 | 0.8455   | 0.8457   | 0.1378 | 0.0499 |
| 70.3705       | 50.0  | 50000 | 72.4419         | 0.8455   | 0.2823     | 1.9713 | 0.8455   | 0.8458   | 0.1379 | 0.0501 |


### Framework versions

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

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