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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: 1.2368
- Accuracy: 0.5312

## 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: 7e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.2726          | 0.575    |
| No log        | 2.0   | 10   | 1.3480          | 0.5062   |
| No log        | 3.0   | 15   | 1.2696          | 0.5375   |
| No log        | 4.0   | 20   | 1.2715          | 0.5312   |
| No log        | 5.0   | 25   | 1.2360          | 0.5687   |
| No log        | 6.0   | 30   | 1.2728          | 0.5125   |
| No log        | 7.0   | 35   | 1.2374          | 0.525    |
| No log        | 8.0   | 40   | 1.2484          | 0.5437   |
| No log        | 9.0   | 45   | 1.2336          | 0.5563   |
| No log        | 10.0  | 50   | 1.2128          | 0.6      |


### Framework versions

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

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