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# vit-base-patch16-224-in21k-finetuned-cifar10

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 cifar-10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1126
- Accuracy: 0.9877

## Model description

More information needed

## Intended uses & limitations

More information needed

## How to Get Started with the Model

```Python
from transformers import pipeline

pipe = pipeline("image-classification", "avanishd/vit-base-patch16-224-in21k-finetuned-cifar10")
pipe(image)
```

## Training and evaluation data

More information needed

## Training procedure

More information needed

### 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: 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: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4166        | 1.0   | 313  | 0.2324          | 0.9791   |
| 0.3247        | 2.0   | 626  | 0.1320          | 0.9875   |
| 0.2661        | 2.992 | 936  | 0.1126          | 0.9877   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

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