Trash Detection API is an open-source project leveraging YOLO (You Only Look Once) for detecting trash in images. This project uses FastAPI for the backend server and serves a simple web interface for uploading images and viewing detection results.

This API uses a model that has been directly trained for multi-object waste classification. If you need the original model, please contact us here with the purpose of use.

• Real-time Object Detection: Detects trash in images using a custom-trained YOLO model optimized for trash detection.
• Fast and Efficient: Uses FastAPI for handling requests at lightning speed.
• User-Friendly Interface: Simple web interface for uploading images and viewing detection results.
• Base64 Image Encoding: Returns images with detected trash in Base64 format for easy integration.

To get started with Trash Detection, follow these steps:

1. Clone the Repository

   git clone https://github.com/Antraxmin/Trash-Detection-API.git
   cd trash-detection-api

2. Create a Virtual Environment and Install Dependencies

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Download the YOLO Model

   Place your YOLO model (best.pt) in the models directory.

4. Run the Server

   uvicorn main:app --reload

5. Access the Web Interface

   Open your browser and go to http://127.0.0.1:8000.

curl -X POST "http://localhost:8000/predict/" -F "file=@image.jpg"

• Upload Image: Use the web interface to upload an image.
• View Results: The server will return the image with detected trash highlighted in bounding boxes, along with the count of detected objects.

For any inquiries or questions, please contact antraxmin@gmail.com.