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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: 1.3640
- Accuracy: 0.5188

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 2.0560          | 0.2      |
| 2.0089        | 2.0   | 10   | 1.9447          | 0.3      |
| 2.0089        | 3.0   | 15   | 1.6725          | 0.4062   |
| 1.4709        | 4.0   | 20   | 1.4705          | 0.4313   |
| 1.4709        | 5.0   | 25   | 1.3500          | 0.5      |
| 0.7193        | 6.0   | 30   | 1.4467          | 0.4562   |
| 0.7193        | 7.0   | 35   | 1.3640          | 0.5188   |
| 0.2139        | 8.0   | 40   | 1.5223          | 0.45     |
| 0.2139        | 9.0   | 45   | 1.4146          | 0.5188   |
| 0.1101        | 10.0  | 50   | 1.4256          | 0.5062   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0

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