---
title: Bird vs Drone Classification
emoji: 🦅🛸
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
---

# Bird vs Drone Image Classification

An end-to-end deep learning project to classify airborne objects into "Bird" or "Drone" categories using a Convolutional Neural Network (MobileNetV2).

## Features
- **Deep Learning Model**: MobileNetV2 based architecture for fast and accurate classification.
- **Data Pipeline**: Automated conversion from YOLO detection labels to classification datasets.
- **Web Interface**: Premium glassmorphic UI for real-time inference.
- **Data Augmentation**: Robust training using rotation, flip, and zoom augmentations.

## Project Structure
- `prepare_data.py`: Prepares the dataset manifests.
- `train_model.py`: Trains the model on a subset of the 20k+ images.
- `app.py`: Flask application for the web interface.
- `templates/` & `static/`: Frontend assets.
- `bird_vs_drone_model.h5`: The trained model weights.

## Installation
```bash
pip install -r requirements.txt
```

## Usage
1. **Prepare Data**:
   ```bash
   python prepare_data.py
   ```
2. **Train Model**:
   ```bash
   python train_model.py
   ```
3. **Run Web App**:
   ```bash
   python app.py
   ```

## Results
The system provides a confidence score and a visual analysis of the uploaded target, distinguishing between natural avian flight and synthetic drone movement.