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# vit-small_rvl_cdip

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: 0.0854
- Accuracy: 0.9110
- Brier Loss: 0.1305
- Nll: 1.2339
- F1 Micro: 0.9110
- F1 Macro: 0.9112
- Ece: 0.0097
- Aurc: 0.0122

## 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.2638        | 1.0   | 2500  | 0.2093          | 0.8574   | 0.2035     | 1.6580 | 0.8574   | 0.8564   | 0.0105 | 0.0272 |
| 0.1559        | 2.0   | 5000  | 0.1413          | 0.8854   | 0.1645     | 1.5062 | 0.8854   | 0.8863   | 0.0101 | 0.0185 |
| 0.1036        | 3.0   | 7500  | 0.1212          | 0.8994   | 0.1461     | 1.4353 | 0.8994   | 0.8999   | 0.0096 | 0.0150 |
| 0.0693        | 4.0   | 10000 | 0.1196          | 0.9013   | 0.1440     | 1.3827 | 0.9013   | 0.9017   | 0.0152 | 0.0148 |
| 0.0451        | 5.0   | 12500 | 0.1094          | 0.9062   | 0.1391     | 1.3099 | 0.9062   | 0.9064   | 0.0124 | 0.0139 |
| 0.0317        | 6.0   | 15000 | 0.0997          | 0.9073   | 0.1357     | 1.2889 | 0.9073   | 0.9078   | 0.0091 | 0.0132 |
| 0.0224        | 7.0   | 17500 | 0.0961          | 0.9081   | 0.1348     | 1.2705 | 0.9081   | 0.9082   | 0.0100 | 0.0129 |
| 0.0166        | 8.0   | 20000 | 0.0890          | 0.9099   | 0.1328     | 1.2484 | 0.9099   | 0.9102   | 0.0085 | 0.0126 |
| 0.0117        | 9.0   | 22500 | 0.0862          | 0.9096   | 0.1316     | 1.2428 | 0.9096   | 0.9100   | 0.0100 | 0.0123 |
| 0.0085        | 10.0  | 25000 | 0.0854          | 0.9110   | 0.1305     | 1.2339 | 0.9110   | 0.9112   | 0.0097 | 0.0122 |


### Framework versions

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

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