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# dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_hint

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: 2.5149
- Accuracy: 0.8438
- Brier Loss: 0.2886
- Nll: 1.9666
- F1 Micro: 0.8438
- F1 Macro: 0.8442
- Ece: 0.1400
- Aurc: 0.0499

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 2.8286        | 1.0   | 1000  | 2.6354          | 0.6627   | 0.4563     | 2.2971 | 0.6627   | 0.6604   | 0.0542 | 0.1330 |
| 2.3           | 2.0   | 2000  | 2.2674          | 0.73     | 0.3761     | 2.0481 | 0.7300   | 0.7314   | 0.0509 | 0.0916 |
| 2.0283        | 3.0   | 3000  | 2.0891          | 0.7602   | 0.3360     | 1.9964 | 0.7602   | 0.7626   | 0.0564 | 0.0728 |
| 1.8552        | 4.0   | 4000  | 2.1367          | 0.746    | 0.3686     | 2.0430 | 0.746    | 0.7485   | 0.0911 | 0.0815 |
| 1.7095        | 5.0   | 5000  | 2.0469          | 0.7725   | 0.3301     | 1.9740 | 0.7725   | 0.7715   | 0.0882 | 0.0683 |
| 1.6118        | 6.0   | 6000  | 1.9706          | 0.7788   | 0.3199     | 1.9470 | 0.7788   | 0.7773   | 0.0816 | 0.0617 |
| 1.4475        | 7.0   | 7000  | 2.0324          | 0.779    | 0.3364     | 2.0056 | 0.779    | 0.7789   | 0.1159 | 0.0640 |
| 1.3546        | 8.0   | 8000  | 2.0987          | 0.7955   | 0.3266     | 1.9823 | 0.7955   | 0.7965   | 0.1259 | 0.0597 |
| 1.2711        | 9.0   | 9000  | 2.1830          | 0.7863   | 0.3487     | 2.0545 | 0.7863   | 0.7879   | 0.1418 | 0.0621 |
| 1.1984        | 10.0  | 10000 | 2.2992          | 0.7923   | 0.3537     | 2.0028 | 0.7923   | 0.7925   | 0.1532 | 0.0612 |
| 1.1503        | 11.0  | 11000 | 2.3319          | 0.795    | 0.3449     | 2.0241 | 0.795    | 0.7946   | 0.1527 | 0.0594 |
| 1.0998        | 12.0  | 12000 | 2.4733          | 0.7973   | 0.3553     | 2.0856 | 0.7973   | 0.7964   | 0.1602 | 0.0589 |
| 1.0752        | 13.0  | 13000 | 2.4884          | 0.7887   | 0.3655     | 2.0351 | 0.7887   | 0.7902   | 0.1679 | 0.0644 |
| 1.0564        | 14.0  | 14000 | 2.4374          | 0.7963   | 0.3496     | 2.0512 | 0.7963   | 0.7985   | 0.1611 | 0.0570 |
| 1.0227        | 15.0  | 15000 | 2.5464          | 0.7973   | 0.3582     | 2.1184 | 0.7973   | 0.7936   | 0.1676 | 0.0568 |
| 1.0129        | 16.0  | 16000 | 2.5022          | 0.8027   | 0.3441     | 2.0449 | 0.8027   | 0.8036   | 0.1636 | 0.0560 |
| 0.9895        | 17.0  | 17000 | 2.4877          | 0.811    | 0.3358     | 2.0303 | 0.811    | 0.8099   | 0.1578 | 0.0562 |
| 0.9628        | 18.0  | 18000 | 2.4552          | 0.8107   | 0.3328     | 2.0399 | 0.8108   | 0.8114   | 0.1548 | 0.0527 |
| 0.9466        | 19.0  | 19000 | 2.5208          | 0.818    | 0.3251     | 2.0761 | 0.818    | 0.8189   | 0.1524 | 0.0520 |
| 0.9291        | 20.0  | 20000 | 2.5858          | 0.8137   | 0.3332     | 2.0634 | 0.8137   | 0.8141   | 0.1588 | 0.0538 |
| 0.9177        | 21.0  | 21000 | 2.5647          | 0.8107   | 0.3383     | 2.0875 | 0.8108   | 0.8124   | 0.1601 | 0.0539 |
| 0.9038        | 22.0  | 22000 | 2.6104          | 0.82     | 0.3301     | 2.1033 | 0.82     | 0.8198   | 0.1566 | 0.0559 |
| 0.8874        | 23.0  | 23000 | 2.5864          | 0.8237   | 0.3188     | 2.0000 | 0.8237   | 0.8244   | 0.1517 | 0.0519 |
| 0.8858        | 24.0  | 24000 | 2.5969          | 0.8185   | 0.3273     | 2.0714 | 0.8185   | 0.8191   | 0.1551 | 0.0527 |
| 0.8653        | 25.0  | 25000 | 2.5529          | 0.828    | 0.3109     | 2.0179 | 0.828    | 0.8287   | 0.1505 | 0.0509 |
| 0.8475        | 26.0  | 26000 | 2.5745          | 0.8265   | 0.3171     | 1.9994 | 0.8265   | 0.8272   | 0.1509 | 0.0526 |
| 0.8569        | 27.0  | 27000 | 2.5906          | 0.8265   | 0.3142     | 2.0156 | 0.8265   | 0.8272   | 0.1499 | 0.0565 |
| 0.8368        | 28.0  | 28000 | 2.7150          | 0.8225   | 0.3271     | 2.0439 | 0.8225   | 0.8215   | 0.1561 | 0.0580 |
| 0.8355        | 29.0  | 29000 | 2.6501          | 0.824    | 0.3211     | 1.9908 | 0.824    | 0.8260   | 0.1545 | 0.0541 |
| 0.832         | 30.0  | 30000 | 2.5656          | 0.8315   | 0.3076     | 2.0091 | 0.8315   | 0.8328   | 0.1474 | 0.0540 |
| 0.8191        | 31.0  | 31000 | 2.6891          | 0.827    | 0.3189     | 1.9819 | 0.827    | 0.8294   | 0.1529 | 0.0573 |
| 0.8118        | 32.0  | 32000 | 2.6791          | 0.827    | 0.3175     | 2.0233 | 0.827    | 0.8268   | 0.1523 | 0.0575 |
| 0.8098        | 33.0  | 33000 | 2.5437          | 0.8373   | 0.2992     | 1.9926 | 0.8373   | 0.8384   | 0.1435 | 0.0492 |
| 0.8006        | 34.0  | 34000 | 2.5751          | 0.8415   | 0.2932     | 2.0036 | 0.8415   | 0.8410   | 0.1403 | 0.0501 |
| 0.8033        | 35.0  | 35000 | 2.5944          | 0.8303   | 0.3113     | 2.0069 | 0.8303   | 0.8302   | 0.1492 | 0.0537 |
| 0.7916        | 36.0  | 36000 | 2.4955          | 0.839    | 0.2922     | 1.9523 | 0.839    | 0.8391   | 0.1407 | 0.0493 |
| 0.7919        | 37.0  | 37000 | 2.6199          | 0.8365   | 0.3003     | 1.9494 | 0.8365   | 0.8370   | 0.1458 | 0.0538 |
| 0.7844        | 38.0  | 38000 | 2.5823          | 0.8365   | 0.3011     | 1.9960 | 0.8365   | 0.8368   | 0.1462 | 0.0511 |
| 0.7795        | 39.0  | 39000 | 2.5626          | 0.8415   | 0.2928     | 1.9916 | 0.8415   | 0.8412   | 0.1406 | 0.0484 |
| 0.7757        | 40.0  | 40000 | 2.5528          | 0.8415   | 0.2891     | 1.9512 | 0.8415   | 0.8420   | 0.1406 | 0.0502 |
| 0.7788        | 41.0  | 41000 | 2.5829          | 0.8383   | 0.2983     | 1.9420 | 0.8383   | 0.8377   | 0.1438 | 0.0530 |
| 0.7743        | 42.0  | 42000 | 2.5285          | 0.838    | 0.2948     | 1.9636 | 0.838    | 0.8383   | 0.1449 | 0.0486 |
| 0.768         | 43.0  | 43000 | 2.5130          | 0.8405   | 0.2906     | 1.9448 | 0.8405   | 0.8404   | 0.1418 | 0.0479 |
| 0.7681        | 44.0  | 44000 | 2.5347          | 0.8383   | 0.2951     | 1.9588 | 0.8383   | 0.8390   | 0.1446 | 0.0508 |
| 0.7662        | 45.0  | 45000 | 2.5246          | 0.8413   | 0.2900     | 1.9314 | 0.8413   | 0.8416   | 0.1415 | 0.0508 |
| 0.7629        | 46.0  | 46000 | 2.5246          | 0.8397   | 0.2913     | 1.9648 | 0.8397   | 0.8403   | 0.1433 | 0.0498 |
| 0.7632        | 47.0  | 47000 | 2.5217          | 0.8425   | 0.2892     | 1.9648 | 0.8425   | 0.8429   | 0.1409 | 0.0503 |
| 0.7598        | 48.0  | 48000 | 2.5163          | 0.8435   | 0.2881     | 1.9776 | 0.8435   | 0.8439   | 0.1407 | 0.0500 |
| 0.7609        | 49.0  | 49000 | 2.5187          | 0.8438   | 0.2885     | 1.9677 | 0.8438   | 0.8442   | 0.1401 | 0.0500 |
| 0.759         | 50.0  | 50000 | 2.5149          | 0.8438   | 0.2886     | 1.9666 | 0.8438   | 0.8442   | 0.1400 | 0.0499 |


### Framework versions

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

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