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# vit-base_rvl-cdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5

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: 3.6996
- Accuracy: 0.856
- Brier Loss: 0.2370
- Nll: 1.1806
- F1 Micro: 0.856
- F1 Macro: 0.8578
- Ece: 0.1015
- Aurc: 0.0302

## 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: 96
- eval_batch_size: 96
- 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log        | 1.0   | 167  | 4.6372          | 0.632    | 0.5161     | 2.2084 | 0.632    | 0.6245   | 0.1480 | 0.1490 |
| No log        | 2.0   | 334  | 4.2247          | 0.7143   | 0.3976     | 1.8912 | 0.7142   | 0.7110   | 0.0878 | 0.0942 |
| 4.8599        | 3.0   | 501  | 4.0290          | 0.7488   | 0.3551     | 1.7330 | 0.7488   | 0.7552   | 0.0711 | 0.0785 |
| 4.8599        | 4.0   | 668  | 3.8716          | 0.7903   | 0.2981     | 1.6409 | 0.7903   | 0.7898   | 0.0468 | 0.0593 |
| 4.8599        | 5.0   | 835  | 3.7535          | 0.8055   | 0.2829     | 1.5302 | 0.8055   | 0.8039   | 0.0465 | 0.0521 |
| 3.7258        | 6.0   | 1002 | 3.7365          | 0.8023   | 0.2787     | 1.5134 | 0.8023   | 0.8043   | 0.0352 | 0.0509 |
| 3.7258        | 7.0   | 1169 | 3.7092          | 0.811    | 0.2705     | 1.3930 | 0.811    | 0.8130   | 0.0472 | 0.0488 |
| 3.7258        | 8.0   | 1336 | 3.6799          | 0.8213   | 0.2643     | 1.4444 | 0.8213   | 0.8242   | 0.0484 | 0.0453 |
| 3.4329        | 9.0   | 1503 | 3.6148          | 0.8265   | 0.2522     | 1.3355 | 0.8265   | 0.8295   | 0.0522 | 0.0425 |
| 3.4329        | 10.0  | 1670 | 3.5723          | 0.826    | 0.2524     | 1.3332 | 0.826    | 0.8286   | 0.0637 | 0.0398 |
| 3.4329        | 11.0  | 1837 | 3.6298          | 0.8277   | 0.2565     | 1.3664 | 0.8277   | 0.8304   | 0.0720 | 0.0422 |
| 3.2987        | 12.0  | 2004 | 3.5604          | 0.8407   | 0.2376     | 1.3420 | 0.8407   | 0.8424   | 0.0609 | 0.0359 |
| 3.2987        | 13.0  | 2171 | 3.5885          | 0.8393   | 0.2446     | 1.3552 | 0.8393   | 0.8420   | 0.0712 | 0.0381 |
| 3.2987        | 14.0  | 2338 | 3.6191          | 0.8315   | 0.2518     | 1.3329 | 0.8315   | 0.8322   | 0.0772 | 0.0383 |
| 3.2268        | 15.0  | 2505 | 3.5920          | 0.837    | 0.2465     | 1.3397 | 0.8370   | 0.8399   | 0.0816 | 0.0372 |
| 3.2268        | 16.0  | 2672 | 3.5483          | 0.847    | 0.2324     | 1.2733 | 0.847    | 0.8489   | 0.0697 | 0.0337 |
| 3.2268        | 17.0  | 2839 | 3.5924          | 0.8438   | 0.2444     | 1.2686 | 0.8438   | 0.8450   | 0.0838 | 0.0355 |
| 3.174         | 18.0  | 3006 | 3.5909          | 0.8427   | 0.2419     | 1.2631 | 0.8427   | 0.8449   | 0.0856 | 0.0336 |
| 3.174         | 19.0  | 3173 | 3.5857          | 0.8452   | 0.2393     | 1.2979 | 0.8452   | 0.8474   | 0.0804 | 0.0338 |
| 3.174         | 20.0  | 3340 | 3.5700          | 0.8455   | 0.2373     | 1.2916 | 0.8455   | 0.8471   | 0.0824 | 0.0336 |
| 3.1369        | 21.0  | 3507 | 3.5578          | 0.8518   | 0.2298     | 1.2615 | 0.8518   | 0.8531   | 0.0779 | 0.0316 |
| 3.1369        | 22.0  | 3674 | 3.5659          | 0.8478   | 0.2349     | 1.2532 | 0.8478   | 0.8502   | 0.0848 | 0.0325 |
| 3.1369        | 23.0  | 3841 | 3.5506          | 0.8552   | 0.2302     | 1.2530 | 0.8552   | 0.8572   | 0.0817 | 0.0312 |
| 3.1077        | 24.0  | 4008 | 3.5551          | 0.857    | 0.2298     | 1.2669 | 0.857    | 0.8585   | 0.0817 | 0.0306 |
| 3.1077        | 25.0  | 4175 | 3.5563          | 0.8575   | 0.2259     | 1.2374 | 0.8575   | 0.8587   | 0.0820 | 0.0296 |
| 3.1077        | 26.0  | 4342 | 3.5642          | 0.8555   | 0.2312     | 1.2159 | 0.8555   | 0.8577   | 0.0855 | 0.0305 |
| 3.0885        | 27.0  | 4509 | 3.5739          | 0.856    | 0.2332     | 1.2143 | 0.856    | 0.8581   | 0.0854 | 0.0309 |
| 3.0885        | 28.0  | 4676 | 3.5544          | 0.855    | 0.2294     | 1.2305 | 0.855    | 0.8567   | 0.0860 | 0.0302 |
| 3.0885        | 29.0  | 4843 | 3.5574          | 0.8598   | 0.2262     | 1.2330 | 0.8598   | 0.8616   | 0.0839 | 0.0304 |
| 3.0716        | 30.0  | 5010 | 3.5673          | 0.8572   | 0.2291     | 1.2208 | 0.8572   | 0.8591   | 0.0888 | 0.0298 |
| 3.0716        | 31.0  | 5177 | 3.5818          | 0.853    | 0.2293     | 1.1947 | 0.853    | 0.8550   | 0.0917 | 0.0302 |
| 3.0716        | 32.0  | 5344 | 3.5792          | 0.858    | 0.2295     | 1.2086 | 0.858    | 0.8597   | 0.0881 | 0.0297 |
| 3.064         | 33.0  | 5511 | 3.5895          | 0.8575   | 0.2315     | 1.2009 | 0.8575   | 0.8590   | 0.0900 | 0.0296 |
| 3.064         | 34.0  | 5678 | 3.5923          | 0.8565   | 0.2293     | 1.1905 | 0.8565   | 0.8583   | 0.0901 | 0.0295 |
| 3.064         | 35.0  | 5845 | 3.5997          | 0.8562   | 0.2310     | 1.2128 | 0.8562   | 0.8577   | 0.0922 | 0.0297 |
| 3.0572        | 36.0  | 6012 | 3.6041          | 0.8572   | 0.2307     | 1.1932 | 0.8572   | 0.8589   | 0.0917 | 0.0296 |
| 3.0572        | 37.0  | 6179 | 3.6123          | 0.857    | 0.2319     | 1.1984 | 0.857    | 0.8587   | 0.0932 | 0.0294 |
| 3.0572        | 38.0  | 6346 | 3.6162          | 0.8585   | 0.2304     | 1.1909 | 0.8585   | 0.8600   | 0.0917 | 0.0293 |
| 3.0542        | 39.0  | 6513 | 3.6318          | 0.8575   | 0.2321     | 1.1982 | 0.8575   | 0.8590   | 0.0945 | 0.0298 |
| 3.0542        | 40.0  | 6680 | 3.6319          | 0.8572   | 0.2324     | 1.1905 | 0.8572   | 0.8588   | 0.0944 | 0.0296 |
| 3.0542        | 41.0  | 6847 | 3.6401          | 0.856    | 0.2327     | 1.1953 | 0.856    | 0.8577   | 0.0964 | 0.0295 |
| 3.0527        | 42.0  | 7014 | 3.6567          | 0.8572   | 0.2343     | 1.1821 | 0.8572   | 0.8588   | 0.0968 | 0.0299 |
| 3.0527        | 43.0  | 7181 | 3.6601          | 0.8562   | 0.2341     | 1.1885 | 0.8562   | 0.8580   | 0.0981 | 0.0299 |
| 3.0527        | 44.0  | 7348 | 3.6683          | 0.8572   | 0.2351     | 1.1854 | 0.8572   | 0.8588   | 0.0977 | 0.0299 |
| 3.0479        | 45.0  | 7515 | 3.6742          | 0.8568   | 0.2353     | 1.1894 | 0.8568   | 0.8584   | 0.0986 | 0.0299 |
| 3.0479        | 46.0  | 7682 | 3.6847          | 0.8565   | 0.2360     | 1.1813 | 0.8565   | 0.8582   | 0.1002 | 0.0301 |
| 3.0479        | 47.0  | 7849 | 3.6891          | 0.8562   | 0.2363     | 1.1814 | 0.8562   | 0.8581   | 0.1004 | 0.0302 |
| 3.0469        | 48.0  | 8016 | 3.6964          | 0.8558   | 0.2367     | 1.1806 | 0.8558   | 0.8575   | 0.1014 | 0.0302 |
| 3.0469        | 49.0  | 8183 | 3.6982          | 0.8565   | 0.2369     | 1.1808 | 0.8565   | 0.8583   | 0.1008 | 0.0302 |
| 3.0469        | 50.0  | 8350 | 3.6996          | 0.856    | 0.2370     | 1.1806 | 0.856    | 0.8578   | 0.1015 | 0.0302 |


### Framework versions

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

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