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# image_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 the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4916
- Accuracy: 0.4688

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 2.0695          | 0.1812   |
| No log        | 2.0   | 40   | 2.0566          | 0.2062   |
| No log        | 3.0   | 60   | 2.0300          | 0.2625   |
| No log        | 4.0   | 80   | 1.9731          | 0.3125   |
| No log        | 5.0   | 100  | 1.8858          | 0.3375   |
| No log        | 6.0   | 120  | 1.7904          | 0.3438   |
| No log        | 7.0   | 140  | 1.7051          | 0.3875   |
| No log        | 8.0   | 160  | 1.6312          | 0.4      |
| No log        | 9.0   | 180  | 1.5429          | 0.45     |
| No log        | 10.0  | 200  | 1.4916          | 0.4688   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

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