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# results

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: 1.3718
- Accuracy: 0.45

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8675        | 1.0   | 40   | 1.7931          | 0.3125   |
| 1.568         | 2.0   | 80   | 1.5873          | 0.3688   |
| 1.3605        | 3.0   | 120  | 1.5087          | 0.4375   |
| 1.0784        | 4.0   | 160  | 1.4299          | 0.45     |
| 0.8568        | 5.0   | 200  | 1.4141          | 0.475    |
| 0.649         | 6.0   | 240  | 1.4242          | 0.4562   |
| 0.4787        | 7.0   | 280  | 1.3718          | 0.45     |
| 0.359         | 8.0   | 320  | 1.3828          | 0.45     |
| 0.3032        | 9.0   | 360  | 1.3888          | 0.4688   |
| 0.2782        | 10.0  | 400  | 1.3995          | 0.4437   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0

label_0 label_1 label_2 label_3 label_4 label_5 label_6 label_7