---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- food
- fruits
- junkfood
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-food-classification-gutkia01
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-food-classification-gutkia01

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the food-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0008
- Model Preparation Time: 0.0034
- Accuracy: 0.9998

# 🍎 Fruits vs. Junkfood Classifier – Vision Transformer (gutkia01)

This model is a fine-tuned version of [`google/vit-base-patch16-224`](https://huggingface.co/google/vit-base-patch16-224), trained on a custom binary dataset to distinguish between **healthy fruits** and **unhealthy fast food**.

## 🧠 Model Description

- **Architecture:** Vision Transformer (ViT)
- **Base model:** `google/vit-base-patch16-224`
- **Task:** Binary image classification: `Fruit` vs. `Junkfood`
- **Framework:** Hugging Face Transformers Trainer
- **Input format:** RGB images, 224×224, loaded via `imagefolder`

## ✅ Intended Use & Limitations

### Appropriate Use Cases

- Food classification in nutrition, health, or educational applications
- Interactive demos comparing healthy vs. unhealthy food
- Computer vision use cases with simple binary class structures

### Limitations

- Only supports binary classification (no subclass differentiation)
- Cannot recognize new or abstract dishes (e.g. salad, sushi)
- Cannot evaluate ingredients, calories, or portion sizes

## 📊 Training and Evaluation Data

The model was trained on a binary dataset composed of:

- **Fruits360 Dataset**: 137,000+ structured fruit images in a controlled studio setup ([Kaggle link](https://www.kaggle.com/datasets/moltean/fruits))
- **Fast Food Classification Dataset v2**: 20,000 fast food images, 10 categories (e.g., burger, pizza, fries) ([Kaggle link](https://www.kaggle.com/datasets/utkarshsaxenadn/fast-food-classification-dataset))

### Dataset Composition

The dataset is a combination of:

- **Fruits360 Dataset** ([Kaggle](https://www.kaggle.com/datasets/moltean/fruits))  
- **Fast Food Classification Dataset v2** ([Kaggle](https://www.kaggle.com/datasets/utkarshsaxenadn/fast-food-classification-dataset))

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.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: 6

### Training results


| Epoch | Training Loss | Validation Loss | Accuracy |
|-------|---------------|------------------|----------|
| 1     | 0.0000        | 0.0215           | 0.9975   |
| 2     | 0.0000        | 0.00004          | 1.0000   |
| 3     | 0.0000        | 0.00008          | 1.0000   |
| 4     | 0.0000        | 0.00011          | 1.0000   |
| 5     | 0.0000        | 0.00011          | 1.0000   |
| 6     | 0.0000        | 0.00008          | 1.0000   |

Final training loss: **0.00047**  
Evaluation accuracy: **0.9998**

### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1