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# vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_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: 37.0129
- Accuracy: 0.8277
- Brier Loss: 0.3307
- Nll: 1.8775
- F1 Micro: 0.8277
- F1 Macro: 0.8289
- Ece: 0.1649
- Aurc: 0.0944

## 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: 64
- eval_batch_size: 64
- 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   | 250   | 56.2115         | 0.3142   | 0.8385     | 3.5992 | 0.3142   | 0.2499   | 0.1012 | 0.5692 |
| 56.615        | 2.0   | 500   | 54.0327         | 0.4025   | 0.9176     | 3.1629 | 0.4025   | 0.3116   | 0.4002 | 0.3781 |
| 56.615        | 3.0   | 750   | 49.9569         | 0.4728   | 0.8906     | 2.8997 | 0.4728   | 0.4076   | 0.4129 | 0.2864 |
| 50.7474       | 4.0   | 1000  | 47.4945         | 0.5685   | 0.7670     | 2.6755 | 0.5685   | 0.5350   | 0.3561 | 0.2844 |
| 50.7474       | 5.0   | 1250  | 45.5054         | 0.6378   | 0.6629     | 2.5408 | 0.6378   | 0.6030   | 0.3212 | 0.1851 |
| 45.4907       | 6.0   | 1500  | 43.9471         | 0.679    | 0.5949     | 2.6322 | 0.679    | 0.6636   | 0.2925 | 0.1474 |
| 45.4907       | 7.0   | 1750  | 42.9273         | 0.7342   | 0.4843     | 2.4382 | 0.7342   | 0.7365   | 0.2245 | 0.1436 |
| 42.5191       | 8.0   | 2000  | 41.9715         | 0.7548   | 0.4560     | 2.3596 | 0.7548   | 0.7533   | 0.2231 | 0.1400 |
| 42.5191       | 9.0   | 2250  | 41.4349         | 0.7722   | 0.4310     | 2.3144 | 0.7722   | 0.7718   | 0.2103 | 0.1304 |
| 40.8849       | 10.0  | 2500  | 41.0961         | 0.7805   | 0.4187     | 2.2268 | 0.7805   | 0.7826   | 0.2047 | 0.1305 |
| 40.8849       | 11.0  | 2750  | 40.5831         | 0.7893   | 0.4030     | 2.1663 | 0.7893   | 0.7930   | 0.2001 | 0.1246 |
| 39.8394       | 12.0  | 3000  | 40.1596         | 0.7987   | 0.3877     | 2.1719 | 0.7987   | 0.8015   | 0.1929 | 0.1162 |
| 39.8394       | 13.0  | 3250  | 39.8469         | 0.8033   | 0.3821     | 2.1455 | 0.8033   | 0.8077   | 0.1889 | 0.1183 |
| 38.9442       | 14.0  | 3500  | 39.5865         | 0.8055   | 0.3761     | 2.1121 | 0.8055   | 0.8096   | 0.1864 | 0.1110 |
| 38.9442       | 15.0  | 3750  | 39.4686         | 0.81     | 0.3693     | 2.0948 | 0.81     | 0.8125   | 0.1831 | 0.1114 |
| 38.3612       | 16.0  | 4000  | 39.1387         | 0.8207   | 0.3446     | 1.9957 | 0.8207   | 0.8219   | 0.1716 | 0.1038 |
| 38.3612       | 17.0  | 4250  | 38.8950         | 0.8143   | 0.3575     | 2.0339 | 0.8143   | 0.8152   | 0.1781 | 0.1034 |
| 37.7855       | 18.0  | 4500  | 38.6442         | 0.8215   | 0.3442     | 1.9658 | 0.8215   | 0.8236   | 0.1718 | 0.1036 |
| 37.7855       | 19.0  | 4750  | 38.5218         | 0.8197   | 0.3477     | 1.9627 | 0.8197   | 0.8220   | 0.1735 | 0.1070 |
| 37.3649       | 20.0  | 5000  | 38.3474         | 0.8225   | 0.3413     | 1.9886 | 0.8225   | 0.8239   | 0.1710 | 0.1028 |
| 37.3649       | 21.0  | 5250  | 38.2377         | 0.8257   | 0.3358     | 1.9864 | 0.8257   | 0.8269   | 0.1674 | 0.0957 |
| 37.0326       | 22.0  | 5500  | 38.1089         | 0.824    | 0.3418     | 1.9404 | 0.824    | 0.8257   | 0.1678 | 0.0980 |
| 37.0326       | 23.0  | 5750  | 37.9861         | 0.8273   | 0.3339     | 1.9540 | 0.8273   | 0.8285   | 0.1664 | 0.0985 |
| 36.7372       | 24.0  | 6000  | 37.8397         | 0.8255   | 0.3376     | 1.9492 | 0.8255   | 0.8268   | 0.1685 | 0.0944 |
| 36.7372       | 25.0  | 6250  | 37.7772         | 0.8253   | 0.3370     | 1.9078 | 0.8253   | 0.8255   | 0.1669 | 0.0997 |
| 36.4341       | 26.0  | 6500  | 37.6550         | 0.828    | 0.3325     | 1.9388 | 0.828    | 0.8284   | 0.1647 | 0.0943 |
| 36.4341       | 27.0  | 6750  | 37.5873         | 0.8255   | 0.3364     | 1.9319 | 0.8255   | 0.8261   | 0.1680 | 0.0920 |
| 36.2152       | 28.0  | 7000  | 37.5052         | 0.825    | 0.3379     | 1.8945 | 0.825    | 0.8268   | 0.1681 | 0.0981 |
| 36.2152       | 29.0  | 7250  | 37.4586         | 0.8243   | 0.3361     | 1.9094 | 0.8243   | 0.8251   | 0.1692 | 0.0945 |
| 36.0128       | 30.0  | 7500  | 37.3730         | 0.8277   | 0.3304     | 1.9062 | 0.8277   | 0.8288   | 0.1657 | 0.0946 |
| 36.0128       | 31.0  | 7750  | 37.3309         | 0.8277   | 0.3309     | 1.9045 | 0.8277   | 0.8291   | 0.1660 | 0.0947 |
| 35.8486       | 32.0  | 8000  | 37.2620         | 0.8267   | 0.3323     | 1.8884 | 0.8267   | 0.8279   | 0.1652 | 0.0950 |
| 35.8486       | 33.0  | 8250  | 37.2147         | 0.8275   | 0.3308     | 1.9079 | 0.8275   | 0.8290   | 0.1654 | 0.0960 |
| 35.6854       | 34.0  | 8500  | 37.1911         | 0.831    | 0.3252     | 1.8935 | 0.831    | 0.8323   | 0.1613 | 0.0939 |
| 35.6854       | 35.0  | 8750  | 37.1523         | 0.8283   | 0.3301     | 1.8847 | 0.8283   | 0.8293   | 0.1644 | 0.0972 |
| 35.5758       | 36.0  | 9000  | 37.1315         | 0.8305   | 0.3252     | 1.8941 | 0.8305   | 0.8317   | 0.1627 | 0.0934 |
| 35.5758       | 37.0  | 9250  | 37.1184         | 0.8275   | 0.3320     | 1.8844 | 0.8275   | 0.8285   | 0.1654 | 0.0923 |
| 35.4911       | 38.0  | 9500  | 37.1149         | 0.827    | 0.3327     | 1.8885 | 0.827    | 0.8288   | 0.1668 | 0.0953 |
| 35.4911       | 39.0  | 9750  | 37.1067         | 0.8267   | 0.3323     | 1.8846 | 0.8267   | 0.8281   | 0.1659 | 0.0932 |
| 35.4248       | 40.0  | 10000 | 37.0792         | 0.8293   | 0.3294     | 1.8840 | 0.8293   | 0.8305   | 0.1633 | 0.0937 |
| 35.4248       | 41.0  | 10250 | 37.0798         | 0.8297   | 0.3288     | 1.8718 | 0.8297   | 0.8309   | 0.1639 | 0.0929 |
| 35.3648       | 42.0  | 10500 | 37.0635         | 0.8265   | 0.3351     | 1.8883 | 0.8265   | 0.8279   | 0.1680 | 0.0951 |
| 35.3648       | 43.0  | 10750 | 37.0470         | 0.828    | 0.3308     | 1.8746 | 0.828    | 0.8294   | 0.1656 | 0.0939 |
| 35.2961       | 44.0  | 11000 | 37.0305         | 0.8273   | 0.3321     | 1.8901 | 0.8273   | 0.8286   | 0.1657 | 0.0932 |
| 35.2961       | 45.0  | 11250 | 37.0261         | 0.8275   | 0.3315     | 1.8823 | 0.8275   | 0.8287   | 0.1650 | 0.0949 |
| 35.241        | 46.0  | 11500 | 37.0253         | 0.827    | 0.3311     | 1.8751 | 0.827    | 0.8283   | 0.1662 | 0.0940 |
| 35.241        | 47.0  | 11750 | 37.0200         | 0.8277   | 0.3321     | 1.8708 | 0.8277   | 0.8289   | 0.1653 | 0.0949 |
| 35.2059       | 48.0  | 12000 | 37.0165         | 0.8277   | 0.3305     | 1.8745 | 0.8277   | 0.8289   | 0.1650 | 0.0934 |
| 35.2059       | 49.0  | 12250 | 37.0130         | 0.8275   | 0.3312     | 1.8743 | 0.8275   | 0.8287   | 0.1655 | 0.0942 |
| 35.18         | 50.0  | 12500 | 37.0129         | 0.8277   | 0.3307     | 1.8775 | 0.8277   | 0.8289   | 0.1649 | 0.0944 |


### Framework versions

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

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