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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: 1.5254
- Accuracy: 0.4313

## 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: 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 1.9225          | 0.25     |
| No log        | 2.0   | 80   | 1.5751          | 0.4188   |
| No log        | 3.0   | 120  | 1.5159          | 0.3875   |
| No log        | 4.0   | 160  | 1.5254          | 0.4313   |
| 1.2986        | 5.0   | 200  | 1.6747          | 0.4125   |
| 1.2986        | 6.0   | 240  | 1.8746          | 0.3563   |
| 1.2986        | 7.0   | 280  | 2.1805          | 0.35     |
| 1.2986        | 8.0   | 320  | 2.3406          | 0.3125   |
| 1.2986        | 9.0   | 360  | 2.3001          | 0.3438   |
| 0.7028        | 10.0  | 400  | 2.2428          | 0.2625   |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

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