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# vit-snack-classification-fp16

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2821
- Accuracy: 0.9445

## 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: 5e-05
- 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
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5962        | 0.33  | 100  | 0.4781          | 0.9497   |
| 0.4348        | 0.66  | 200  | 0.3871          | 0.9466   |
| 0.3349        | 0.99  | 300  | 0.3750          | 0.9309   |
| 0.2495        | 1.32  | 400  | 0.2920          | 0.9476   |
| 0.2331        | 1.65  | 500  | 0.2821          | 0.9445   |
| 0.3387        | 1.98  | 600  | 0.2597          | 0.9476   |
| 0.3133        | 2.31  | 700  | 0.2536          | 0.9487   |
| 0.2615        | 2.64  | 800  | 0.2393          | 0.9529   |
| 0.2687        | 2.97  | 900  | 0.2392          | 0.9550   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2

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