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# results

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5130
- Accuracy: 0.9678
- F1 Macro: 0.3279

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.9582        | 1.0   | 326  | 0.6083          | 0.9570   | 0.3720   |
| 0.9225        | 2.0   | 652  | 0.5519          | 0.9455   | 0.3759   |
| 0.8664        | 3.0   | 978  | 0.4927          | 0.9677   | 0.3454   |
| 0.6536        | 4.0   | 1304 | 0.5522          | 0.8848   | 0.3702   |
| 0.6793        | 5.0   | 1630 | 0.4951          | 0.9455   | 0.3830   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1

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