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# vit-fruits-classifier-without-augmentation

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the Custom fruit image dataset (uploaded from GitHub) without augmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1472
- Accuracy: 0.9654

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1326        | 1.0   | 520  | 0.1220          | 0.9692   |
| 0.0852        | 2.0   | 1040 | 0.1124          | 0.9721   |
| 0.0668        | 3.0   | 1560 | 0.1111          | 0.9712   |
| 0.0492        | 4.0   | 2080 | 0.1083          | 0.9721   |
| 0.059         | 5.0   | 2600 | 0.1085          | 0.9721   |


### Framework versions

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

acai apple apricot avocado banana black_berry coconut corn_kernel cranberry dragonfruit durian eggplant elderberry fig grape grapefruit hard_kiwi indian_strawberry jalapeno mandarine mango papaya passion_fruit pineapple pomegranate raspberry