---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: fruits-classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: Schram03/fruits
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9280373831775701
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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