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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: 1.2786
- Accuracy: 0.55

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 80   | 2.0304          | 0.2188   |
| 2.0496        | 2.0   | 160  | 1.8376          | 0.35     |
| 1.7855        | 3.0   | 240  | 1.6403          | 0.4375   |
| 1.4719        | 4.0   | 320  | 1.5324          | 0.525    |
| 1.2454        | 5.0   | 400  | 1.4388          | 0.5312   |
| 1.2454        | 6.0   | 480  | 1.3698          | 0.575    |
| 1.0235        | 7.0   | 560  | 1.3360          | 0.5563   |
| 0.8674        | 8.0   | 640  | 1.3001          | 0.5687   |
| 0.7613        | 9.0   | 720  | 1.2869          | 0.55     |
| 0.6857        | 10.0  | 800  | 1.2786          | 0.55     |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1

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