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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.
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