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# vit-base-patch16-224-13_model

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: 0.5185
- Accuracy: 0.835

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 9

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7535        | 0.9787 | 23   | 1.3773          | 0.545    |
| 0.9606        | 2.0    | 47   | 1.1264          | 0.625    |
| 0.5199        | 2.9787 | 70   | 0.7703          | 0.705    |
| 0.3037        | 4.0    | 94   | 0.6922          | 0.745    |
| 0.1607        | 4.9787 | 117  | 0.5718          | 0.81     |
| 0.148         | 6.0    | 141  | 0.5436          | 0.82     |
| 0.1238        | 6.9787 | 164  | 0.5454          | 0.805    |
| 0.0889        | 8.0    | 188  | 0.5023          | 0.84     |
| 0.0745        | 8.8085 | 207  | 0.5185          | 0.835    |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

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