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# vit-base-patch16-224-in21k-tiny_rvl_cdip-NK1000_hint

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 75.3146
- Accuracy: 0.811
- Brier Loss: 0.3395
- Nll: 2.0856
- F1 Micro: 0.811
- F1 Macro: 0.8109
- Ece: 0.1625
- Aurc: 0.0586

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 78.9278       | 1.0   | 1000  | 78.3645         | 0.588    | 0.5476     | 2.4320 | 0.588    | 0.5825   | 0.0552 | 0.1928 |
| 77.6109       | 2.0   | 2000  | 77.4909         | 0.684    | 0.4259     | 2.1686 | 0.684    | 0.6879   | 0.0496 | 0.1167 |
| 77.1849       | 3.0   | 3000  | 77.1828         | 0.7185   | 0.3854     | 2.1032 | 0.7185   | 0.7200   | 0.0495 | 0.0932 |
| 76.8526       | 4.0   | 4000  | 76.9800         | 0.748    | 0.3549     | 2.0716 | 0.748    | 0.7492   | 0.0768 | 0.0781 |
| 76.5928       | 5.0   | 5000  | 76.7544         | 0.743    | 0.3576     | 2.0634 | 0.743    | 0.7461   | 0.0564 | 0.0846 |
| 76.1507       | 6.0   | 6000  | 76.5850         | 0.7688   | 0.3354     | 2.0506 | 0.7688   | 0.7698   | 0.0857 | 0.0701 |
| 75.8107       | 7.0   | 7000  | 76.5816         | 0.75     | 0.3766     | 2.1753 | 0.75     | 0.7542   | 0.1230 | 0.0815 |
| 75.868        | 8.0   | 8000  | 76.5048         | 0.785    | 0.3324     | 2.0865 | 0.785    | 0.7869   | 0.1244 | 0.0623 |
| 75.6016       | 9.0   | 9000  | 76.3919         | 0.7827   | 0.3475     | 2.0858 | 0.7828   | 0.7849   | 0.1340 | 0.0657 |
| 75.4883       | 10.0  | 10000 | 76.5121         | 0.7768   | 0.3644     | 2.1372 | 0.7768   | 0.7747   | 0.1550 | 0.0662 |
| 75.2568       | 11.0  | 11000 | 76.4107         | 0.7857   | 0.3603     | 2.1229 | 0.7857   | 0.7863   | 0.1561 | 0.0619 |
| 75.1623       | 12.0  | 12000 | 76.4517         | 0.771    | 0.3857     | 2.1118 | 0.771    | 0.7721   | 0.1681 | 0.0702 |
| 75.0021       | 13.0  | 13000 | 76.3632         | 0.7885   | 0.3635     | 2.1178 | 0.7885   | 0.7870   | 0.1607 | 0.0621 |
| 74.9056       | 14.0  | 14000 | 76.3074         | 0.7925   | 0.3533     | 2.1361 | 0.7925   | 0.7926   | 0.1626 | 0.0573 |
| 74.9295       | 15.0  | 15000 | 76.3445         | 0.785    | 0.3730     | 2.0515 | 0.785    | 0.7861   | 0.1694 | 0.0661 |
| 74.7288       | 16.0  | 16000 | 76.3441         | 0.7845   | 0.3776     | 2.1216 | 0.7845   | 0.7828   | 0.1731 | 0.0666 |
| 74.5985       | 17.0  | 17000 | 76.1255         | 0.794    | 0.3593     | 2.0759 | 0.7940   | 0.7969   | 0.1640 | 0.0605 |
| 74.471        | 18.0  | 18000 | 76.2140         | 0.7863   | 0.3721     | 2.1872 | 0.7863   | 0.7861   | 0.1705 | 0.0638 |
| 74.4457       | 19.0  | 19000 | 76.1380         | 0.7925   | 0.3650     | 2.1106 | 0.7925   | 0.7940   | 0.1708 | 0.0634 |
| 74.3675       | 20.0  | 20000 | 76.1423         | 0.7897   | 0.3684     | 2.0882 | 0.7897   | 0.7910   | 0.1731 | 0.0642 |
| 74.3618       | 21.0  | 21000 | 76.0578         | 0.7987   | 0.3604     | 2.1007 | 0.7987   | 0.7982   | 0.1676 | 0.0622 |
| 74.1398       | 22.0  | 22000 | 75.9928         | 0.7997   | 0.3578     | 2.0590 | 0.7997   | 0.8008   | 0.1672 | 0.0624 |
| 74.0834       | 23.0  | 23000 | 75.8857         | 0.8013   | 0.3561     | 2.0986 | 0.8013   | 0.8010   | 0.1662 | 0.0602 |
| 74.1467       | 24.0  | 24000 | 75.8767         | 0.8      | 0.3605     | 2.0794 | 0.8000   | 0.8014   | 0.1682 | 0.0608 |
| 73.8823       | 25.0  | 25000 | 75.9471         | 0.799    | 0.3564     | 2.0934 | 0.799    | 0.7997   | 0.1684 | 0.0619 |
| 73.9657       | 26.0  | 26000 | 75.8618         | 0.7987   | 0.3594     | 2.1020 | 0.7987   | 0.7991   | 0.1703 | 0.0599 |
| 73.9721       | 27.0  | 27000 | 75.7331         | 0.8145   | 0.3347     | 2.0514 | 0.8145   | 0.8144   | 0.1569 | 0.0571 |
| 73.8298       | 28.0  | 28000 | 75.8175         | 0.8007   | 0.3582     | 2.0923 | 0.8007   | 0.7999   | 0.1714 | 0.0625 |
| 73.8483       | 29.0  | 29000 | 75.7541         | 0.8023   | 0.3554     | 2.1075 | 0.8023   | 0.8002   | 0.1698 | 0.0603 |
| 73.6726       | 30.0  | 30000 | 75.6642         | 0.8095   | 0.3454     | 2.0600 | 0.8095   | 0.8092   | 0.1638 | 0.0600 |
| 73.7118       | 31.0  | 31000 | 75.5905         | 0.8105   | 0.3398     | 2.1354 | 0.8105   | 0.8106   | 0.1587 | 0.0595 |
| 73.5938       | 32.0  | 32000 | 75.5721         | 0.8087   | 0.3429     | 2.0765 | 0.8087   | 0.8094   | 0.1640 | 0.0616 |
| 73.5563       | 33.0  | 33000 | 75.7021         | 0.8085   | 0.3474     | 2.0825 | 0.8085   | 0.8092   | 0.1656 | 0.0633 |
| 73.6469       | 34.0  | 34000 | 75.5322         | 0.8095   | 0.3406     | 2.0907 | 0.8095   | 0.8079   | 0.1632 | 0.0590 |
| 73.4666       | 35.0  | 35000 | 75.4994         | 0.8105   | 0.3397     | 2.0839 | 0.8105   | 0.8102   | 0.1621 | 0.0590 |
| 73.4144       | 36.0  | 36000 | 75.5095         | 0.8063   | 0.3476     | 2.1055 | 0.8062   | 0.8050   | 0.1666 | 0.0616 |
| 73.2744       | 37.0  | 37000 | 75.4980         | 0.8117   | 0.3403     | 2.0693 | 0.8117   | 0.8123   | 0.1607 | 0.0569 |
| 73.4358       | 38.0  | 38000 | 75.4824         | 0.809    | 0.3434     | 2.0996 | 0.809    | 0.8090   | 0.1645 | 0.0586 |
| 73.2696       | 39.0  | 39000 | 75.5088         | 0.8085   | 0.3468     | 2.0697 | 0.8085   | 0.8089   | 0.1658 | 0.0589 |
| 73.382        | 40.0  | 40000 | 75.4705         | 0.8095   | 0.3437     | 2.0738 | 0.8095   | 0.8104   | 0.1641 | 0.0621 |
| 73.3006       | 41.0  | 41000 | 75.4697         | 0.809    | 0.3440     | 2.1203 | 0.809    | 0.8097   | 0.1624 | 0.0614 |
| 73.4237       | 42.0  | 42000 | 75.3601         | 0.8093   | 0.3434     | 2.0736 | 0.8093   | 0.8094   | 0.1629 | 0.0575 |
| 73.2571       | 43.0  | 43000 | 75.3364         | 0.8103   | 0.3398     | 2.0665 | 0.8103   | 0.8101   | 0.1630 | 0.0599 |
| 73.2241       | 44.0  | 44000 | 75.3369         | 0.8135   | 0.3381     | 2.0609 | 0.8135   | 0.8136   | 0.1600 | 0.0581 |
| 73.2271       | 45.0  | 45000 | 75.2917         | 0.814    | 0.3355     | 2.0906 | 0.8140   | 0.8142   | 0.1576 | 0.0582 |
| 73.1427       | 46.0  | 46000 | 75.3108         | 0.8125   | 0.3377     | 2.0784 | 0.8125   | 0.8127   | 0.1613 | 0.0586 |
| 73.2754       | 47.0  | 47000 | 75.3195         | 0.8127   | 0.3386     | 2.0860 | 0.8128   | 0.8127   | 0.1604 | 0.0586 |
| 73.132        | 48.0  | 48000 | 75.3168         | 0.812    | 0.3391     | 2.0853 | 0.8120   | 0.8118   | 0.1612 | 0.0582 |
| 73.1482       | 49.0  | 49000 | 75.2943         | 0.8117   | 0.3395     | 2.0895 | 0.8117   | 0.8116   | 0.1615 | 0.0586 |
| 73.1849       | 50.0  | 50000 | 75.3146         | 0.811    | 0.3395     | 2.0856 | 0.811    | 0.8109   | 0.1625 | 0.0586 |


### Framework versions

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

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