PigeonDetect is an AI-powered tool designed to accurately detect and classify different species of pigeons using image data. It leverages machine learning and computer vision techniques to provide quick and reliable pigeon species identification.
- High Accuracy: Utilizes advanced machine learning models to ensure precise species identification.
- Real-Time Detection: Capable of processing images and providing results in real-time.
- User-Friendly Interface: Simple and intuitive interface for easy usage.
- Scalable: Can be integrated into larger systems or applications for comprehensive wildlife monitoring.
- Java Development Kit (JDK) 8 or higher
- Maven 3.6.0 or higher
- TensorFlow for Java
- Image dataset of pigeon species for training (if training from scratch)
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Clone the repository
git clone https://github.com/pingvinkowalski/PigeonDetect.git cd PigeonDetect -
Build the project
mvn clean install
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Run the application
java -jar target/pigeon-detect-1.0.0.jar
To run PigeonDetect using Docker, follow these steps:
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Build the Docker image
docker build -t pigeon-detect:latest . -
Run the Docker container
docker run -d -p 8080:8080 pigeon-detect:latest
You can connect to PigeonDetect using any HTTP client. The default port is 8080.
Example using curl:
curl -X POST "http://localhost:8080/predict" -F "file=@path_to_your_image.jpg"