---
license: mit
tags:
- vision
- food-recognition
- ingredients
- utensils
- portion-size
- computer-vision
- mobile
- ug-food-dataset
---

# UG Food Detection Model

This model identifies food ingredients, utensils, and estimates portion sizes from images.

## Model Description

This Vision Transformer (ViT) model is trained on the UG Food Dataset to recognize:
- Food ingredients: Various food items and ingredients
- Kitchen utensils: Cooking tools and equipment
- Portion sizes: Measurement estimates

## Classes
The model can identify 40 classes.

## Usage

```python
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# Load model and processor
processor = ViTImageProcessor.from_pretrained("ssevan/ug-food-detector")
model = ViTForImageClassification.from_pretrained("ssevan/ug-food-detector")

# Process image
image = Image.open('food_image.jpg')
inputs = processor(image, return_tensors='pt')

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class_idx = torch.argmax(probabilities, dim=1).item()

print(f'Predicted class index: {predicted_class_idx}')
```

## Mobile Usage
This model is optimized for mobile deployment.