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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_og_simkd

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: 261.8253
- Accuracy: 0.845
- Brier Loss: 0.2896
- Nll: 1.8917
- F1 Micro: 0.845
- F1 Macro: 0.8458
- Ece: 0.1431
- Aurc: 0.0597

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 284.1528      | 1.0   | 1000  | 282.4832        | 0.5725   | 0.6206     | 2.4811 | 0.5725   | 0.5320   | 0.2175 | 0.1848 |
| 281.942       | 2.0   | 2000  | 280.8445        | 0.6943   | 0.4704     | 2.2781 | 0.6943   | 0.6823   | 0.1809 | 0.1131 |
| 281.4574      | 3.0   | 3000  | 280.4852        | 0.7185   | 0.4524     | 2.1204 | 0.7185   | 0.7250   | 0.1810 | 0.1008 |
| 279.8457      | 4.0   | 4000  | 278.7610        | 0.769    | 0.3964     | 2.0520 | 0.769    | 0.7685   | 0.1789 | 0.0775 |
| 279.1982      | 5.0   | 5000  | 278.0784        | 0.792    | 0.3522     | 1.9832 | 0.792    | 0.7915   | 0.1570 | 0.0670 |
| 278.1353      | 6.0   | 6000  | 277.1822        | 0.8135   | 0.3198     | 1.8943 | 0.8135   | 0.8160   | 0.1427 | 0.0547 |
| 277.4303      | 7.0   | 7000  | 275.9198        | 0.8193   | 0.3170     | 1.9321 | 0.8193   | 0.8203   | 0.1453 | 0.0589 |
| 276.2535      | 8.0   | 8000  | 274.8677        | 0.8273   | 0.3063     | 1.8543 | 0.8273   | 0.8266   | 0.1404 | 0.0538 |
| 275.1405      | 9.0   | 9000  | 273.8240        | 0.8345   | 0.2905     | 1.8312 | 0.8345   | 0.8362   | 0.1369 | 0.0525 |
| 274.3982      | 10.0  | 10000 | 273.2765        | 0.835    | 0.2892     | 1.8405 | 0.835    | 0.8362   | 0.1363 | 0.0512 |
| 272.9251      | 11.0  | 11000 | 272.4844        | 0.8455   | 0.2730     | 1.8874 | 0.8455   | 0.8468   | 0.1277 | 0.0478 |
| 272.1662      | 12.0  | 12000 | 271.4586        | 0.8373   | 0.2923     | 1.8514 | 0.8373   | 0.8374   | 0.1396 | 0.0508 |
| 272.1504      | 13.0  | 13000 | 271.0098        | 0.8452   | 0.2765     | 1.8428 | 0.8452   | 0.8454   | 0.1304 | 0.0505 |
| 271.0841      | 14.0  | 14000 | 270.4739        | 0.8405   | 0.2884     | 1.8279 | 0.8405   | 0.8421   | 0.1368 | 0.0522 |
| 270.5412      | 15.0  | 15000 | 269.5290        | 0.843    | 0.2861     | 1.8339 | 0.843    | 0.8434   | 0.1375 | 0.0524 |
| 269.4117      | 16.0  | 16000 | 269.1779        | 0.842    | 0.2874     | 1.8357 | 0.842    | 0.8422   | 0.1383 | 0.0520 |
| 269.1644      | 17.0  | 17000 | 268.5929        | 0.8465   | 0.2743     | 1.8563 | 0.8465   | 0.8470   | 0.1333 | 0.0491 |
| 268.7355      | 18.0  | 18000 | 268.2595        | 0.8475   | 0.2790     | 1.8540 | 0.8475   | 0.8479   | 0.1345 | 0.0505 |
| 268.3442      | 19.0  | 19000 | 267.7969        | 0.8508   | 0.2749     | 1.8406 | 0.8508   | 0.8509   | 0.1307 | 0.0505 |
| 267.4279      | 20.0  | 20000 | 267.2394        | 0.844    | 0.2811     | 1.8676 | 0.844    | 0.8448   | 0.1384 | 0.0509 |
| 267.468       | 21.0  | 21000 | 267.0267        | 0.8525   | 0.2694     | 1.8311 | 0.8525   | 0.8534   | 0.1293 | 0.0519 |
| 266.6685      | 22.0  | 22000 | 266.3500        | 0.8485   | 0.2772     | 1.8471 | 0.8485   | 0.8487   | 0.1368 | 0.0507 |
| 266.4612      | 23.0  | 23000 | 265.8022        | 0.8433   | 0.2863     | 1.8363 | 0.8433   | 0.8441   | 0.1399 | 0.0536 |
| 266.3148      | 24.0  | 24000 | 265.7575        | 0.8488   | 0.2783     | 1.8835 | 0.8488   | 0.8495   | 0.1366 | 0.0518 |
| 265.0058      | 25.0  | 25000 | 265.1237        | 0.8468   | 0.2841     | 1.8232 | 0.8468   | 0.8476   | 0.1370 | 0.0555 |
| 265.3975      | 26.0  | 26000 | 265.0540        | 0.8518   | 0.2757     | 1.8747 | 0.8518   | 0.8525   | 0.1324 | 0.0527 |
| 265.4347      | 27.0  | 27000 | 264.8875        | 0.8502   | 0.2755     | 1.8525 | 0.8502   | 0.8509   | 0.1339 | 0.0515 |
| 264.4956      | 28.0  | 28000 | 264.4421        | 0.8448   | 0.2864     | 1.8596 | 0.8448   | 0.8457   | 0.1402 | 0.0535 |
| 264.3941      | 29.0  | 29000 | 264.0486        | 0.8472   | 0.2815     | 1.8533 | 0.8472   | 0.8480   | 0.1379 | 0.0538 |
| 264.138       | 30.0  | 30000 | 264.2021        | 0.8495   | 0.2772     | 1.8547 | 0.8495   | 0.8500   | 0.1363 | 0.0531 |
| 263.8278      | 31.0  | 31000 | 263.6598        | 0.8472   | 0.2840     | 1.8715 | 0.8472   | 0.8472   | 0.1393 | 0.0549 |
| 263.4683      | 32.0  | 32000 | 263.4160        | 0.8465   | 0.2820     | 1.8844 | 0.8465   | 0.8471   | 0.1389 | 0.0558 |
| 263.5281      | 33.0  | 33000 | 263.2498        | 0.851    | 0.2788     | 1.8720 | 0.851    | 0.8520   | 0.1361 | 0.0554 |
| 263.3538      | 34.0  | 34000 | 262.9030        | 0.8472   | 0.2839     | 1.9007 | 0.8472   | 0.8482   | 0.1393 | 0.0562 |
| 262.673       | 35.0  | 35000 | 262.9031        | 0.8452   | 0.2859     | 1.8754 | 0.8452   | 0.8463   | 0.1406 | 0.0564 |
| 262.9104      | 36.0  | 36000 | 262.8404        | 0.8468   | 0.2867     | 1.8730 | 0.8468   | 0.8478   | 0.1398 | 0.0561 |
| 262.9824      | 37.0  | 37000 | 262.8044        | 0.849    | 0.2810     | 1.8759 | 0.849    | 0.8494   | 0.1372 | 0.0524 |
| 262.2614      | 38.0  | 38000 | 262.8396        | 0.8458   | 0.2861     | 1.8657 | 0.8458   | 0.8468   | 0.1410 | 0.0548 |
| 262.2726      | 39.0  | 39000 | 262.3623        | 0.846    | 0.2833     | 1.8772 | 0.8460   | 0.8465   | 0.1405 | 0.0565 |
| 262.3102      | 40.0  | 40000 | 262.4073        | 0.8465   | 0.2831     | 1.8798 | 0.8465   | 0.8475   | 0.1395 | 0.0553 |
| 262.2994      | 41.0  | 41000 | 262.2219        | 0.8472   | 0.2836     | 1.8810 | 0.8472   | 0.8475   | 0.1399 | 0.0579 |
| 262.222       | 42.0  | 42000 | 262.4181        | 0.8472   | 0.2775     | 1.8712 | 0.8472   | 0.8482   | 0.1389 | 0.0552 |
| 261.6536      | 43.0  | 43000 | 262.2162        | 0.8465   | 0.2844     | 1.8668 | 0.8465   | 0.8479   | 0.1401 | 0.0565 |
| 261.9964      | 44.0  | 44000 | 262.1039        | 0.8472   | 0.2848     | 1.8718 | 0.8472   | 0.8481   | 0.1403 | 0.0590 |
| 261.4522      | 45.0  | 45000 | 261.7883        | 0.846    | 0.2868     | 1.8589 | 0.8460   | 0.8459   | 0.1419 | 0.0556 |
| 261.6668      | 46.0  | 46000 | 262.0215        | 0.8492   | 0.2822     | 1.8682 | 0.8492   | 0.8494   | 0.1385 | 0.0542 |
| 261.8742      | 47.0  | 47000 | 261.9067        | 0.847    | 0.2846     | 1.8765 | 0.847    | 0.8476   | 0.1403 | 0.0599 |
| 261.5992      | 48.0  | 48000 | 261.7719        | 0.8475   | 0.2820     | 1.8854 | 0.8475   | 0.8485   | 0.1401 | 0.0583 |
| 261.6406      | 49.0  | 49000 | 261.5148        | 0.846    | 0.2873     | 1.8737 | 0.8460   | 0.8466   | 0.1427 | 0.0598 |
| 261.9611      | 50.0  | 50000 | 261.8253        | 0.845    | 0.2896     | 1.8917 | 0.845    | 0.8458   | 0.1431 | 0.0597 |


### Framework versions

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

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