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# cifarv2

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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2653
- Accuracy: 0.921

## 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
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6994        | 1.0   | 250  | 0.7132          | 0.8758   |
| 0.4271        | 2.0   | 500  | 0.4477          | 0.894    |
| 0.3112        | 3.0   | 750  | 0.3905          | 0.8942   |
| 0.3139        | 4.0   | 1000 | 0.3207          | 0.9115   |
| 0.2511        | 5.0   | 1250 | 0.3288          | 0.9048   |
| 0.2652        | 6.0   | 1500 | 0.2977          | 0.9125   |
| 0.2392        | 7.0   | 1750 | 0.2720          | 0.9187   |
| 0.1759        | 8.0   | 2000 | 0.2670          | 0.9173   |
| 0.2024        | 9.0   | 2250 | 0.2606          | 0.9193   |
| 0.1774        | 10.0  | 2500 | 0.2653          | 0.921    |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

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