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# vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_og_simkd

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: 268.5710
- Accuracy: 0.832
- Brier Loss: 0.3051
- Nll: 1.8984
- F1 Micro: 0.832
- F1 Macro: 0.8328
- Ece: 0.1458
- Aurc: 0.0566

## 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: 32
- eval_batch_size: 32
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 286.5314      | 1.0   | 500   | 283.8548        | 0.4555   | 0.8016     | 2.8438 | 0.4555   | 0.3694   | 0.3223 | 0.2744 |
| 283.1635      | 2.0   | 1000  | 282.0722        | 0.5577   | 0.6555     | 2.5676 | 0.5577   | 0.5116   | 0.2428 | 0.1873 |
| 282.0305      | 3.0   | 1500  | 281.5496        | 0.6355   | 0.5598     | 2.4665 | 0.6355   | 0.6232   | 0.2150 | 0.1424 |
| 281.2235      | 4.0   | 2000  | 280.5680        | 0.7065   | 0.4447     | 2.2146 | 0.7065   | 0.7028   | 0.1680 | 0.1010 |
| 280.4245      | 5.0   | 2500  | 279.8773        | 0.7245   | 0.4675     | 2.2237 | 0.7245   | 0.7253   | 0.1993 | 0.1058 |
| 279.6686      | 6.0   | 3000  | 279.1086        | 0.7748   | 0.3740     | 2.0556 | 0.7748   | 0.7729   | 0.1601 | 0.0700 |
| 278.8635      | 7.0   | 3500  | 278.2839        | 0.7675   | 0.3894     | 1.9970 | 0.7675   | 0.7689   | 0.1713 | 0.0791 |
| 278.1131      | 8.0   | 4000  | 277.6409        | 0.7977   | 0.3416     | 1.9272 | 0.7977   | 0.7969   | 0.1478 | 0.0618 |
| 277.4522      | 9.0   | 4500  | 277.2180        | 0.8083   | 0.3280     | 1.9560 | 0.8083   | 0.8122   | 0.1474 | 0.0559 |
| 276.8156      | 10.0  | 5000  | 276.5454        | 0.8145   | 0.3158     | 1.8932 | 0.8145   | 0.8149   | 0.1404 | 0.0531 |
| 276.2433      | 11.0  | 5500  | 275.8951        | 0.8117   | 0.3233     | 1.8813 | 0.8117   | 0.8122   | 0.1460 | 0.0551 |
| 275.7038      | 12.0  | 6000  | 275.6540        | 0.8217   | 0.3033     | 1.8605 | 0.8217   | 0.8228   | 0.1355 | 0.0521 |
| 275.1847      | 13.0  | 6500  | 275.1825        | 0.8263   | 0.3063     | 1.8834 | 0.8263   | 0.8282   | 0.1384 | 0.0513 |
| 274.7168      | 14.0  | 7000  | 274.8503        | 0.8203   | 0.3133     | 1.8742 | 0.8203   | 0.8222   | 0.1448 | 0.0520 |
| 274.2753      | 15.0  | 7500  | 274.2773        | 0.8273   | 0.3015     | 1.8833 | 0.8273   | 0.8282   | 0.1392 | 0.0497 |
| 273.8617      | 16.0  | 8000  | 273.9056        | 0.825    | 0.3018     | 1.8546 | 0.825    | 0.8273   | 0.1391 | 0.0527 |
| 273.4509      | 17.0  | 8500  | 273.4976        | 0.827    | 0.3028     | 1.8656 | 0.827    | 0.8270   | 0.1415 | 0.0500 |
| 273.0643      | 18.0  | 9000  | 273.0985        | 0.8315   | 0.2977     | 1.8671 | 0.8315   | 0.8326   | 0.1382 | 0.0491 |
| 272.7104      | 19.0  | 9500  | 272.9490        | 0.8273   | 0.3035     | 1.8686 | 0.8273   | 0.8285   | 0.1427 | 0.0525 |
| 272.3669      | 20.0  | 10000 | 272.6702        | 0.8253   | 0.3052     | 1.8809 | 0.8253   | 0.8258   | 0.1441 | 0.0499 |
| 272.0331      | 21.0  | 10500 | 272.2651        | 0.833    | 0.2966     | 1.8759 | 0.833    | 0.8340   | 0.1397 | 0.0479 |
| 271.7213      | 22.0  | 11000 | 272.2740        | 0.829    | 0.2999     | 1.8507 | 0.8290   | 0.8295   | 0.1419 | 0.0493 |
| 271.4253      | 23.0  | 11500 | 271.7973        | 0.8327   | 0.2962     | 1.8837 | 0.8327   | 0.8326   | 0.1404 | 0.0490 |
| 271.1327      | 24.0  | 12000 | 271.5110        | 0.8355   | 0.2930     | 1.8580 | 0.8355   | 0.8365   | 0.1393 | 0.0502 |
| 270.858       | 25.0  | 12500 | 271.1653        | 0.828    | 0.3035     | 1.8529 | 0.828    | 0.8287   | 0.1455 | 0.0520 |
| 270.5978      | 26.0  | 13000 | 270.9584        | 0.8283   | 0.3056     | 1.8615 | 0.8283   | 0.8282   | 0.1442 | 0.0535 |
| 270.3636      | 27.0  | 13500 | 270.7707        | 0.832    | 0.3030     | 1.8635 | 0.832    | 0.8331   | 0.1442 | 0.0507 |
| 270.1365      | 28.0  | 14000 | 270.3265        | 0.8287   | 0.3096     | 1.8857 | 0.8287   | 0.8295   | 0.1458 | 0.0551 |
| 269.9005      | 29.0  | 14500 | 270.4089        | 0.8307   | 0.3017     | 1.8722 | 0.8308   | 0.8306   | 0.1441 | 0.0528 |
| 269.6876      | 30.0  | 15000 | 270.2905        | 0.8303   | 0.3043     | 1.8792 | 0.8303   | 0.8308   | 0.1446 | 0.0518 |
| 269.5015      | 31.0  | 15500 | 269.9496        | 0.834    | 0.2997     | 1.8925 | 0.834    | 0.8344   | 0.1413 | 0.0519 |
| 269.3106      | 32.0  | 16000 | 269.8872        | 0.8333   | 0.2990     | 1.8822 | 0.8333   | 0.8333   | 0.1419 | 0.0524 |
| 269.1204      | 33.0  | 16500 | 269.7998        | 0.8303   | 0.3057     | 1.9016 | 0.8303   | 0.8310   | 0.1463 | 0.0541 |
| 268.9658      | 34.0  | 17000 | 269.3946        | 0.8347   | 0.3003     | 1.8922 | 0.8347   | 0.8356   | 0.1423 | 0.0535 |
| 268.8073      | 35.0  | 17500 | 269.5928        | 0.8327   | 0.3035     | 1.8508 | 0.8327   | 0.8332   | 0.1443 | 0.0546 |
| 268.6654      | 36.0  | 18000 | 269.2020        | 0.8307   | 0.3058     | 1.8891 | 0.8308   | 0.8317   | 0.1456 | 0.0543 |
| 268.5213      | 37.0  | 18500 | 269.3784        | 0.8295   | 0.3095     | 1.8732 | 0.8295   | 0.8299   | 0.1478 | 0.0549 |
| 268.3883      | 38.0  | 19000 | 269.0580        | 0.8303   | 0.3060     | 1.8621 | 0.8303   | 0.8303   | 0.1466 | 0.0559 |
| 268.2752      | 39.0  | 19500 | 269.0785        | 0.8317   | 0.3038     | 1.8956 | 0.8317   | 0.8320   | 0.1449 | 0.0534 |
| 268.1814      | 40.0  | 20000 | 268.8612        | 0.8357   | 0.3029     | 1.9057 | 0.8357   | 0.8367   | 0.1416 | 0.0557 |
| 268.0695      | 41.0  | 20500 | 268.8330        | 0.8303   | 0.3047     | 1.8963 | 0.8303   | 0.8309   | 0.1475 | 0.0565 |
| 267.9566      | 42.0  | 21000 | 268.7392        | 0.8313   | 0.3055     | 1.9059 | 0.8313   | 0.8319   | 0.1462 | 0.0550 |
| 267.9045      | 43.0  | 21500 | 268.6012        | 0.8307   | 0.3063     | 1.8974 | 0.8308   | 0.8317   | 0.1478 | 0.0565 |
| 267.8455      | 44.0  | 22000 | 268.8788        | 0.832    | 0.3042     | 1.8960 | 0.832    | 0.8325   | 0.1447 | 0.0553 |
| 267.776       | 45.0  | 22500 | 268.5588        | 0.829    | 0.3093     | 1.9087 | 0.8290   | 0.8296   | 0.1489 | 0.0556 |
| 267.7253      | 46.0  | 23000 | 268.4604        | 0.8303   | 0.3079     | 1.9259 | 0.8303   | 0.8307   | 0.1474 | 0.0573 |
| 267.6786      | 47.0  | 23500 | 268.5825        | 0.8317   | 0.3062     | 1.9043 | 0.8317   | 0.8322   | 0.1464 | 0.0561 |
| 267.6514      | 48.0  | 24000 | 268.4191        | 0.8315   | 0.3072     | 1.8933 | 0.8315   | 0.8320   | 0.1466 | 0.0576 |
| 267.6384      | 49.0  | 24500 | 268.4131        | 0.8303   | 0.3087     | 1.9256 | 0.8303   | 0.8306   | 0.1483 | 0.0576 |
| 267.6158      | 50.0  | 25000 | 268.5710        | 0.832    | 0.3051     | 1.8984 | 0.832    | 0.8328   | 0.1458 | 0.0566 |


### Framework versions

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

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