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# vit-base-patch16-224-franciscoflores-classification

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: 0.0071
- accuracy : 0.9988

## Model description

Transfer learning from a pre-trained image classification model determines 
which images are of a dog and which ones are of food

## Intended uses & limitations

More information needed

## Training and evaluation data

This model was trained using the "sasha/dog-food"

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0618        | 1.9   | 500  | 0.0146          |
| 0.0062        | 3.8   | 1000 | 0.0071          |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

dog food