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# dit-base-finetuned-rvlcdip-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: 2.7080
- Accuracy: 0.8275
- Brier Loss: 0.3142
- Nll: 2.0399
- F1 Micro: 0.8275
- F1 Macro: 0.8270
- Ece: 0.1526
- Aurc: 0.0520

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 3.111         | 1.0   | 1000  | 2.9416          | 0.5917   | 0.5262     | 2.5737 | 0.5917   | 0.5835   | 0.0528 | 0.1821 |
| 2.5832        | 2.0   | 2000  | 2.4518          | 0.6917   | 0.4147     | 2.1569 | 0.6917   | 0.6919   | 0.0508 | 0.1107 |
| 2.2618        | 3.0   | 3000  | 2.2194          | 0.7418   | 0.3548     | 2.0905 | 0.7418   | 0.7417   | 0.0384 | 0.0775 |
| 2.0277        | 4.0   | 4000  | 2.1469          | 0.7575   | 0.3418     | 2.0638 | 0.7575   | 0.7547   | 0.0661 | 0.0729 |
| 1.9024        | 5.0   | 5000  | 2.1380          | 0.7355   | 0.3703     | 2.0583 | 0.7355   | 0.7365   | 0.0619 | 0.0880 |
| 1.7315        | 6.0   | 6000  | 2.0423          | 0.7508   | 0.3495     | 2.0467 | 0.7508   | 0.7566   | 0.0631 | 0.0752 |
| 1.5844        | 7.0   | 7000  | 2.0832          | 0.7628   | 0.3382     | 2.1301 | 0.7628   | 0.7651   | 0.0953 | 0.0689 |
| 1.4761        | 8.0   | 8000  | 2.2224          | 0.773    | 0.3548     | 2.1347 | 0.7730   | 0.7734   | 0.1284 | 0.0708 |
| 1.3852        | 9.0   | 9000  | 2.2341          | 0.7853   | 0.3452     | 2.0905 | 0.7853   | 0.7874   | 0.1349 | 0.0614 |
| 1.3234        | 10.0  | 10000 | 2.3403          | 0.778    | 0.3614     | 2.1125 | 0.778    | 0.7797   | 0.1530 | 0.0649 |
| 1.2546        | 11.0  | 11000 | 2.4153          | 0.7768   | 0.3675     | 2.1438 | 0.7768   | 0.7772   | 0.1601 | 0.0649 |
| 1.2161        | 12.0  | 12000 | 2.5661          | 0.7742   | 0.3810     | 2.1581 | 0.7742   | 0.7752   | 0.1715 | 0.0669 |
| 1.1611        | 13.0  | 13000 | 2.5638          | 0.789    | 0.3616     | 2.0957 | 0.7890   | 0.7888   | 0.1648 | 0.0595 |
| 1.1349        | 14.0  | 14000 | 2.6037          | 0.7957   | 0.3569     | 2.1299 | 0.7957   | 0.7963   | 0.1641 | 0.0578 |
| 1.1043        | 15.0  | 15000 | 2.6763          | 0.7817   | 0.3786     | 2.1078 | 0.7817   | 0.7855   | 0.1755 | 0.0680 |
| 1.0768        | 16.0  | 16000 | 2.6931          | 0.792    | 0.3636     | 2.1056 | 0.792    | 0.7942   | 0.1679 | 0.0601 |
| 1.0675        | 17.0  | 17000 | 2.6384          | 0.7957   | 0.3549     | 2.1658 | 0.7957   | 0.7941   | 0.1651 | 0.0570 |
| 1.0387        | 18.0  | 18000 | 2.8320          | 0.7825   | 0.3899     | 2.1964 | 0.7825   | 0.7804   | 0.1804 | 0.0706 |
| 1.035         | 19.0  | 19000 | 2.7127          | 0.7947   | 0.3641     | 2.0771 | 0.7947   | 0.7981   | 0.1741 | 0.0607 |
| 1.0053        | 20.0  | 20000 | 2.7164          | 0.8035   | 0.3508     | 2.0693 | 0.8035   | 0.8017   | 0.1638 | 0.0594 |
| 0.9783        | 21.0  | 21000 | 2.7162          | 0.8085   | 0.3475     | 2.0165 | 0.8085   | 0.8080   | 0.1622 | 0.0601 |
| 0.9606        | 22.0  | 22000 | 2.7740          | 0.804    | 0.3505     | 2.0738 | 0.804    | 0.8057   | 0.1678 | 0.0585 |
| 0.9579        | 23.0  | 23000 | 2.7597          | 0.803    | 0.3544     | 2.0507 | 0.803    | 0.8038   | 0.1668 | 0.0600 |
| 0.9439        | 24.0  | 24000 | 2.7108          | 0.809    | 0.3407     | 2.0218 | 0.809    | 0.8099   | 0.1626 | 0.0574 |
| 0.9247        | 25.0  | 25000 | 2.6918          | 0.8125   | 0.3355     | 2.0449 | 0.8125   | 0.8114   | 0.1580 | 0.0549 |
| 0.9275        | 26.0  | 26000 | 2.6996          | 0.8163   | 0.3316     | 2.0140 | 0.8163   | 0.8159   | 0.1585 | 0.0582 |
| 0.914         | 27.0  | 27000 | 2.7846          | 0.8113   | 0.3389     | 2.0190 | 0.8113   | 0.8110   | 0.1626 | 0.0598 |
| 0.9036        | 28.0  | 28000 | 2.7436          | 0.817    | 0.3341     | 2.0702 | 0.817    | 0.8166   | 0.1587 | 0.0564 |
| 0.893         | 29.0  | 29000 | 2.7354          | 0.8197   | 0.3272     | 2.0581 | 0.8197   | 0.8207   | 0.1551 | 0.0588 |
| 0.8815        | 30.0  | 30000 | 2.8377          | 0.813    | 0.3414     | 2.1163 | 0.813    | 0.8149   | 0.1630 | 0.0614 |
| 0.8688        | 31.0  | 31000 | 2.7815          | 0.8207   | 0.3310     | 2.0502 | 0.8207   | 0.8205   | 0.1576 | 0.0554 |
| 0.8727        | 32.0  | 32000 | 2.7370          | 0.82     | 0.3292     | 2.1149 | 0.82     | 0.8193   | 0.1563 | 0.0545 |
| 0.8581        | 33.0  | 33000 | 2.8168          | 0.812    | 0.3443     | 2.0026 | 0.8120   | 0.8146   | 0.1658 | 0.0594 |
| 0.8504        | 34.0  | 34000 | 2.7660          | 0.8173   | 0.3321     | 2.0497 | 0.8173   | 0.8181   | 0.1597 | 0.0556 |
| 0.8563        | 35.0  | 35000 | 2.8457          | 0.8097   | 0.3442     | 2.0815 | 0.8097   | 0.8107   | 0.1669 | 0.0592 |
| 0.8415        | 36.0  | 36000 | 2.7366          | 0.8245   | 0.3179     | 2.0282 | 0.8245   | 0.8251   | 0.1511 | 0.0566 |
| 0.8372        | 37.0  | 37000 | 2.7731          | 0.821    | 0.3249     | 2.1084 | 0.821    | 0.8198   | 0.1563 | 0.0546 |
| 0.8406        | 38.0  | 38000 | 2.6948          | 0.8283   | 0.3131     | 2.0343 | 0.8283   | 0.8281   | 0.1493 | 0.0533 |
| 0.831         | 39.0  | 39000 | 2.7781          | 0.827    | 0.3192     | 2.0592 | 0.827    | 0.8270   | 0.1534 | 0.0544 |
| 0.8223        | 40.0  | 40000 | 2.7811          | 0.8267   | 0.3161     | 2.0946 | 0.8267   | 0.8271   | 0.1512 | 0.0570 |
| 0.8258        | 41.0  | 41000 | 2.6993          | 0.827    | 0.3138     | 2.0347 | 0.827    | 0.8271   | 0.1507 | 0.0531 |
| 0.8209        | 42.0  | 42000 | 2.7467          | 0.828    | 0.3197     | 2.0159 | 0.828    | 0.8279   | 0.1530 | 0.0541 |
| 0.8146        | 43.0  | 43000 | 2.7050          | 0.8257   | 0.3159     | 2.0518 | 0.8257   | 0.8249   | 0.1526 | 0.0523 |
| 0.8161        | 44.0  | 44000 | 2.6919          | 0.8257   | 0.3160     | 1.9889 | 0.8257   | 0.8255   | 0.1515 | 0.0530 |
| 0.8121        | 45.0  | 45000 | 2.7314          | 0.8235   | 0.3210     | 2.0259 | 0.8235   | 0.8244   | 0.1542 | 0.0537 |
| 0.809         | 46.0  | 46000 | 2.7203          | 0.8275   | 0.3146     | 2.0431 | 0.8275   | 0.8272   | 0.1526 | 0.0514 |
| 0.8091        | 47.0  | 47000 | 2.7174          | 0.826    | 0.3176     | 2.0313 | 0.826    | 0.8253   | 0.1534 | 0.0527 |
| 0.8073        | 48.0  | 48000 | 2.7058          | 0.8277   | 0.3130     | 2.0258 | 0.8277   | 0.8272   | 0.1515 | 0.0519 |
| 0.8073        | 49.0  | 49000 | 2.7065          | 0.827    | 0.3146     | 2.0301 | 0.827    | 0.8266   | 0.1528 | 0.0523 |
| 0.8069        | 50.0  | 50000 | 2.7080          | 0.8275   | 0.3142     | 2.0399 | 0.8275   | 0.8270   | 0.1526 | 0.0520 |


### Framework versions

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

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