---
title: Image Gradio
emoji: 🌍
colorFrom: yellow
colorTo: blue
sdk: gradio
sdk_version: 5.47.2
app_file: app.py
pinned: false
license: mit
short_description: Stop sign image predictor interface
---

# Stop Sign Detector

A computer vision application that detects whether an image contains a stop sign using AutoGluon's MultiModalPredictor.

## Overview

This application uses deep learning to classify images into two categories:
- **Stop Sign**: Image contains a stop sign
- **Not a Stop Sign**: Image does not contain a stop sign

The model analyzes uploaded images in real-time and provides confidence scores for each class.

## Features

- **Image Upload**: Upload images from your device or capture via webcam
- **Real-time Classification**: Instant predictions as soon as you upload an image
- **Confidence Scores**: See probability distribution across both classes
- **Example Images**: Pre-loaded examples to test the model
- **Multiple Input Sources**: Upload files or use your webcam

## How to Use

1. **Upload an Image**: 
   - Click the image area to upload from your device
   - Or click "webcam" to capture a photo in real-time
2. **View Results**: 
   - The model will automatically analyze the image
   - See the predicted class and confidence percentages
3. **Try Examples**: 
   - Click on the example images to see how the model performs

## Model Details

- **Framework**: AutoGluon MultiModalPredictor
- **Task**: Binary Image Classification
- **Model Repository**: `samder03/2025-24679-image-autogluon-predictor`
- **Input**: RGB images (any size, automatically preprocessed)
- **Output**: Binary classification with probability scores

### Classes

| Class ID | Label | Description |
|----------|-------|-------------|
| 0 | Not a Stop Sign | Image does not contain a stop sign |
| 1 | Stop Sign | Image contains a stop sign |

## Technical Architecture

The application:
1. Accepts images via Gradio interface (upload or webcam)
2. Saves the image temporarily to disk
3. Loads the image into a pandas DataFrame (AutoGluon format)
4. Runs inference using the MultiModalPredictor
5. Returns probability scores for both classes

## Use Cases

- **Traffic Sign Recognition**: Component for autonomous vehicle systems
- **Road Safety Analysis**: Automated traffic sign inventory and monitoring
- **Educational Tool**: Demonstrating computer vision and deep learning
- **Dataset Validation**: Quickly verify stop sign annotations in datasets

## Limitations

- Model is specifically trained for stop signs only
- Performance may vary with:
  - Image quality and resolution
  - Lighting conditions
  - Viewing angles and partial occlusions
  - International stop sign variations
- Not intended for real-time safety-critical applications without further validation

## Performance Considerations

- First prediction may take longer due to model loading
- Subsequent predictions are faster (model cached in memory)
- Image preprocessing is automatic

## Requirements
```txt
gradio
autogluon.multimodal
pandas
Pillow
huggingface_hub