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# vit-base_rvl-cdip

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5535
- Accuracy: 0.897
- Brier Loss: 0.1768
- Nll: 1.0978
- F1 Micro: 0.897
- F1 Macro: 0.8972
- Ece: 0.0801
- Aurc: 0.0180

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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   |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.676         | 1.0   | 5000  | 0.6451          | 0.8230   | 0.2574     | 1.2627 | 0.8230   | 0.8237   | 0.0458 | 0.0425 |
| 0.4207        | 2.0   | 10000 | 0.4251          | 0.8766   | 0.1800     | 1.2821 | 0.8766   | 0.8779   | 0.0154 | 0.0218 |
| 0.3335        | 3.0   | 15000 | 0.3914          | 0.8861   | 0.1676     | 1.2589 | 0.8861   | 0.8858   | 0.0252 | 0.0192 |
| 0.2447        | 4.0   | 20000 | 0.3687          | 0.8934   | 0.1574     | 1.2243 | 0.8934   | 0.8937   | 0.0331 | 0.0164 |
| 0.1623        | 5.0   | 25000 | 0.3843          | 0.8976   | 0.1583     | 1.1553 | 0.8976   | 0.8973   | 0.0461 | 0.0159 |
| 0.1083        | 6.0   | 30000 | 0.4131          | 0.8964   | 0.1624     | 1.1514 | 0.8964   | 0.8967   | 0.0581 | 0.0163 |
| 0.0652        | 7.0   | 35000 | 0.4633          | 0.8966   | 0.1690     | 1.1300 | 0.8966   | 0.8967   | 0.0692 | 0.0169 |
| 0.0361        | 8.0   | 40000 | 0.5068          | 0.8976   | 0.1723     | 1.1161 | 0.8976   | 0.8976   | 0.0737 | 0.0175 |
| 0.0192        | 9.0   | 45000 | 0.5418          | 0.8982   | 0.1748     | 1.1015 | 0.8982   | 0.8983   | 0.0779 | 0.0179 |
| 0.0111        | 10.0  | 50000 | 0.5535          | 0.897    | 0.1768     | 1.0978 | 0.897    | 0.8972   | 0.0801 | 0.0180 |


### Framework versions

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

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