YOLOv10

Quick Start

1. Install Docker

Run the following commands on the development board to install Docker:

# Download installation script
curl -fsSL https://get.docker.com -o get-docker.sh
# Install using Aliyun mirror source
sudo sh get-docker.sh --mirror Aliyun
# Start Docker and enable auto-start on boot
sudo systemctl enable docker
sudo systemctl start docker

2. Run the Project (One command, dual-mode preview)

This RKNN deployment comes from the YOLOv10 modules in reComputer-RK-CV. It implements the model's end-to-end, NMS-free two-stage Top-K post-processing.

Model Information

Step A: Pull Images

sudo docker pull ghcr.io/seeed-projects/recomputer-rk-cv/rk3588-yolov10:latest
sudo docker pull ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-yolov10:latest

Step B: Run with One Click

For RK3576:

sudo docker run --rm --privileged --net=host \
  --device /dev/dri/renderD128:/dev/dri/renderD128 \
  -v /proc/device-tree/compatible:/proc/device-tree/compatible:ro \
  ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-yolov10:latest \
  python3 web_detection.py --platform rk3576 \
  --model_path model/yolov10n.rknn --video_path video/test.mp4

For RK3588:

sudo docker run --rm --privileged --net=host \
  --device /dev/dri/renderD128:/dev/dri/renderD128 \
  -v /proc/device-tree/compatible:/proc/device-tree/compatible:ro \
  ghcr.io/seeed-projects/recomputer-rk-cv/rk3588-yolov10:latest \
  python3 web_detection.py --platform rk3588 \
  --model_path model/yolov10n.rknn --video_path video/test.mp4

Select YOLOv10s with --model_path model/yolov10s.rknn. Replace the video argument with --camera_id -1 for upload-only mode.

🔌 API Documentation

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/yolov10/predict

Request Parameters (Multipart/Form-Data):

• file: Optional image file to detect.
• video: Optional MP4 file.
• timestamp: Optional video timestamp in seconds; defaults to the first frame.
• realtime: Optional boolean; with no upload, use the active camera or local-video frame.
• conf: Optional request-level confidence threshold; defaults to 0.25.
• iou: Accepted for compatibility but not used by YOLOv10's NMS-free post-processing.

Usage Examples:

curl -X POST "http://<BOARD_IP>:8000/api/models/yolov10/predict" \
  -F "file=@bus.jpg" -F "conf=0.25"

Response Format (JSON):

{
  "success": true,
  "source": "uploaded image",
  "predictions": [
    {
      "class": "bus",
      "confidence": 0.91,
      "box": {"x1": 100, "y1": 120, "x2": 520, "y2": 430}
    }
  ],
  "image": {"width": 640, "height": 480}
}

2. System Configuration Interface (Config)

The API accepts iou for compatibility with the standard detection service, but YOLOv10's NMS-free post-process does not use it. The global confidence setting from GET/POST /api/config applies to preview and offline analysis.

The service also exposes health, MJPEG, video upload, asynchronous analysis, status, list, and download endpoints. Runtime files are written under workspace/ by default. A replacement model must retain the input size and output tensors expected by the NMS-free decoder in web_detection.py.

3. Command Line Arguments

Real-time Video Stream Interface (Video Feed)

Get the latest annotated MJPEG stream for browser preview:

• Endpoint: GET /api/video_feed
• Example Usage: <img src="http://<BOARD_IP>:8000/api/video_feed">

🛠️ Developer Guide (Production Recommendations)

Build Local Images

docker build -f docker/rk3576/yolov10.dockerfile \
  -t rk3576-yolov10:local src/rk3576_yolov10

docker build -f docker/rk3588/yolov10.dockerfile \
  -t rk3588-yolov10:local src/rk3588_yolov10