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# vit-tiny_rvl_cdip

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: 0.1016
- Accuracy: 0.9025
- Brier Loss: 0.1427
- Nll: 1.7378
- F1 Micro: 0.9025
- F1 Macro: 0.9029
- Ece: 0.0142
- Aurc: 0.0141

## 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: 128
- eval_batch_size: 128
- 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.3377        | 1.0   | 2500  | 0.2693          | 0.8397   | 0.2295     | 2.0316 | 0.8397   | 0.8399   | 0.0140 | 0.0337 |
| 0.1962        | 2.0   | 5000  | 0.1745          | 0.8717   | 0.1835     | 1.9452 | 0.8717   | 0.8739   | 0.0122 | 0.0222 |
| 0.1359        | 3.0   | 7500  | 0.1380          | 0.8869   | 0.1643     | 1.8585 | 0.8869   | 0.8871   | 0.0089 | 0.0181 |
| 0.099         | 4.0   | 10000 | 0.1297          | 0.8920   | 0.1567     | 1.8113 | 0.8920   | 0.8921   | 0.0128 | 0.0168 |
| 0.068         | 5.0   | 12500 | 0.1253          | 0.8966   | 0.1520     | 1.7963 | 0.8966   | 0.8969   | 0.0120 | 0.0160 |
| 0.0475        | 6.0   | 15000 | 0.1153          | 0.8972   | 0.1487     | 1.7849 | 0.8972   | 0.8979   | 0.0136 | 0.0151 |
| 0.0341        | 7.0   | 17500 | 0.1110          | 0.8995   | 0.1460     | 1.7557 | 0.8995   | 0.8997   | 0.0151 | 0.0146 |
| 0.0238        | 8.0   | 20000 | 0.1059          | 0.9013   | 0.1438     | 1.7503 | 0.9013   | 0.9015   | 0.0120 | 0.0143 |
| 0.017         | 9.0   | 22500 | 0.1034          | 0.9022   | 0.1440     | 1.7344 | 0.9022   | 0.9026   | 0.0142 | 0.0143 |
| 0.0128        | 10.0  | 25000 | 0.1016          | 0.9025   | 0.1427     | 1.7378 | 0.9025   | 0.9029   | 0.0142 | 0.0141 |


### Framework versions

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

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