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# fruits-classification

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the augmented Schram03/fruits dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3039
- Accuracy: 0.9280

## Model description

This model classifies the 33 different types of fruit in the Schram03/fruits dataset. 

## Intended uses & limitations

With further developement it could be possible to differentiat foul fruits or specific subtypes of fruit. This however would need an extensive Dataset.
Currently the model is able to differentiat the trained 33 types of fruit. However, there are limitations especially when using similar fruits like peaches and nectarines.

## Training and evaluation data

80 % Train, 20 % Evaluation

## 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
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5816        | 1.0   | 1070 | 0.5238          | 0.8887   |
| 0.3761        | 2.0   | 2140 | 0.3568          | 0.9182   |
| 0.2934        | 3.0   | 3210 | 0.2852          | 0.9322   |
| 0.2719        | 4.0   | 4280 | 0.2681          | 0.9388   |
| 0.253         | 5.0   | 5350 | 0.2705          | 0.9388   |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1

apple apricot avocado banana blackberrie blueberry cactus fruit cherry clementine cocos grape grapefruit guava kaki kiwi lemon limes lychee mandarine mango maracuja nectarine orange papaya passion fruit peach pear pineapple plum pomegranate quince strawberry watermelon