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# UL_base_classification

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: 0.3125
- Accuracy: 0.8921

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.8296        | 0.9756 | 20   | 0.5683          | 0.8230   |
| 0.4462        | 2.0    | 41   | 0.3949          | 0.8603   |
| 0.3588        | 2.9756 | 61   | 0.3633          | 0.8575   |
| 0.3196        | 4.0    | 82   | 0.3247          | 0.8852   |
| 0.2921        | 4.9756 | 102  | 0.3374          | 0.8728   |
| 0.2688        | 6.0    | 123  | 0.3125          | 0.8921   |
| 0.2366        | 6.8293 | 140  | 0.3137          | 0.8866   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

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