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---
title: Food Classifier with Model Comparison
emoji: 🍔
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 4.19.2
app_file: app.py
pinned: false
---
# 🍔 Food Classifier: Accuracy vs. Speed
This Gradio demo allows you to classify food images using two different transformer-based models and visually compare their performance.
## How to Use
1. **Upload an Image**: Drag and drop a food image or click to upload one. You can also use one of the examples below.
2. **Choose a Model**: Select either the ViT or Swin model from the dropdown.
3. **Click Classify**: The model will predict the food item.
## The Comparison Feature
The key feature of this demo is the **performance comparison chart**:
- **Benchmark Accuracy**: This chart shows the reported accuracy of each model on the Food101 test set. The Swin model is generally more accurate.
- **Inference Time**: This chart shows the *actual time* it took for the selected model to process *your* uploaded image. You can see the speed trade-off firsthand. The ViT model is often faster.
This allows you to understand the classic machine learning trade-off between a model's accuracy and its computational cost (speed).
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