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# vit-base-oxford-iiit-pets

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the fruits-and-vegetables-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1798
- Accuracy: 0.9331

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6158        | 1.0   | 195  | 0.2572          | 0.9145   |
| 0.2671        | 2.0   | 390  | 0.2054          | 0.9288   |
| 0.2212        | 3.0   | 585  | 0.1905          | 0.9288   |
| 0.1935        | 4.0   | 780  | 0.1803          | 0.9345   |
| 0.1969        | 5.0   | 975  | 0.1774          | 0.9316   |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1

apple banana beetroot bell pepper cabbage capsicum carrot cauliflower chilli pepper corn cucumber eggplant garlic ginger grapes jalepeno kiwi lemon lettuce mango onion orange paprika pear peas pineapple pomegranate potato raddish soy beans spinach sweetcorn sweetpotato tomato turnip watermelon