🎯 Yolo prediction service

High performance detection service based on Yolov8 and implemented in rust.

🚀 Characteristics

• ⚡ Asynchronous processing of requests
• 🔍 Real time detection in real time
• 🎨 Support for images in basic format64
• 📊 Structured json output
• 🛡️ Robust management of errors
• 🔄 Optimized Prestimized Images

🏗️ Project structure

├── assets
│   └── coco_classes.txt     # Coco classes for labeling
├── models
│   ├── coco_classes.txt     # Classes configuration
│   ├── coco.yaml            # Coco configuration
│   └── yolov8n.onnx         # YOLO Model pre-trained with COCO dataset
├── outputs                  # Directory Output Predizioni
├── src
│   └── main.rs              # Main source code
└── Dockerfile               # Docker configuration

⚙️ Configuration

The service requires the following ambient variables:

• RUST_LOG: Logging level (es. "info", "debug")
• MODEL_PATH: Path of the onnx model
• CLASSES_PATH: Classes' route route
• HOST: Service host (default: "0.0.0.0")
• PORT: Service door (default: 5000)

🛠️ Build and start

Buil

cargo build --release

Docker use

docker build -t yolo-prediction .
docker run -p 5000:5000 yolo-prediction

🔌 API Endpoints

Prediction objects

• URL: /predict
• Metodo: POST
• Body:

  {
    "image": "base64_encoded_image"
  }

• Risposta:

  {
    "detections": [
      {
        "class": "person",
        "confidence": 0.95,
        "bbox": [x, y, width, height]
      }
    ]
  }

Health control

• URL: /health
• Method: GET
• Answer: 200 OK

📈 Performance

• asynchronous processing to manage multiple requests
• uploading the model only once to startup
• Optimized Pretimized Images
• Configurable confidence Threshold (default: 0.25)

📝 Notes

• The service uses Yolov8 in Onnx format for object detection
• Supports the 80 Coco Standard classes
• Optimized input size: 640x640 pixels