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# vit-base-patch16-224-ethos

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2506
- Accuracy: 0.96

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.8696  | 5    | 0.4608          | 0.87     |
| 0.5337        | 1.9130  | 11   | 0.2743          | 0.91     |
| 0.5337        | 2.9565  | 17   | 0.2239          | 0.94     |
| 0.2275        | 4.0     | 23   | 0.3780          | 0.88     |
| 0.2275        | 4.8696  | 28   | 0.3501          | 0.88     |
| 0.1107        | 5.9130  | 34   | 0.2420          | 0.92     |
| 0.0528        | 6.9565  | 40   | 0.2752          | 0.94     |
| 0.0528        | 8.0     | 46   | 0.3932          | 0.9      |
| 0.0465        | 8.8696  | 51   | 0.2496          | 0.94     |
| 0.0465        | 9.9130  | 57   | 0.3151          | 0.93     |
| 0.0516        | 10.9565 | 63   | 0.1837          | 0.96     |
| 0.0516        | 12.0    | 69   | 0.1885          | 0.95     |
| 0.0317        | 12.8696 | 74   | 0.3941          | 0.92     |
| 0.0463        | 13.9130 | 80   | 0.2577          | 0.95     |
| 0.0463        | 14.9565 | 86   | 0.2128          | 0.95     |
| 0.018         | 16.0    | 92   | 0.2342          | 0.96     |
| 0.018         | 16.8696 | 97   | 0.2483          | 0.96     |
| 0.0179        | 17.3913 | 100  | 0.2506          | 0.96     |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1

rado rolex tissot