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YOLO 11n

YOLO11 Object Detection Series Nano Version, lightweight and efficient, suitable for edge deployment.

Size
~5MB
Memory Requirement
1GB+
Precision
FP16

Getting Started

Choose your platform and inference engine; the Docker command below updates automatically.

Docker
sudo docker run -it --rm --pull always --runtime=nvidia \
  --network host ghcr.io/seeed-studio/vllm:latest-rk3588 \
  vllm serve yolo11n

REST API

Use the REST API to run inference. Copy the commands below.

Curl
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "realtime=true"
# Or without file parameters
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict"
Python
import requests
import json
resp = requests.post(
    "http://127.0.0.1:8000/api/models/yolo11/predict",
    json={},
    timeout=30
)
result = response.json()
print(json.dumps(result, indent=2, ensure_ascii=False))

Model Details

Quick Start

1. Install Docker

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

bash
# 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 project supports previewing through a "web browser". The project can be run with a single command. Additionally, a Fast-API interface is provided to facilitate users' customized development and testing.

Step A: Pull Images

bash
sudo docker pull ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3588-yolo:latest
sudo docker pull ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3576-yolo:latest

Step B: Run with One Click

For RK3588:

bash
sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    --device /dev/video1:/dev/video1 \
    --device /dev/dri/renderD129:/dev/dri/renderD129 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3588-yolo:latest \
    python web_detection.py --model_path model/yolo11n.rknn --camera_id 1

For RK3576:

bash
sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    --device /dev/video1:/dev/video1 \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3576-yolo:latest \
    python web_detection.py --model_path model/yolo11n.rknn --camera_id 1

Access via: http://<Board_IP>:8000

Note: If you need custom classes, you can add -v $(pwd)/class_config.txt:/app/class_config.txt \ mount and --class_path parameter. The program defaults to COCO 80 classes.

Example:

bash
sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    -v $(pwd)/class_config.txt:/app/class_config.txt \
    --device /dev/video1:/dev/video1 \
    --device /dev/dri/renderD129:/dev/dri/renderD129 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/Seeed-Projects/recomputer-rk-cv/rk3588-yolo:latest \
    python web_detection.py --model_path model/yolo11n.rknn --camera_id 1 --class_path class_config.txt

🔌 API Documentation

This project provides RESTful interfaces compatible with the Ultralytics Cloud API standard, supporting object detection via image uploads using HTTP POST requests.

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/yolo11/predict

Request Parameters (Multipart/Form-Data):

  • file: (Optional) Image file to be detected.
  • video: (Optional) MP4 video file to be detected.
  • timestamp: (Optional) Timestamp in the video file (seconds), returns detection results for the frame at that point. Default is 0.
  • realtime: (Optional) Boolean. If true or if no file/video parameters are provided, returns detection results for the current camera frame.
  • conf: (Optional) Confidence threshold for a single request, range 0.0-1.0.
  • iou: (Optional) NMS IOU threshold for a single request, range 0.0-1.0.

Usage Examples:

1. Image Detection:

bash
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "file=@/home/cat/001.jpg"

2. Video Specific Frame Detection:

bash
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "video=@/home/cat/test.mp4" -F "timestamp=5.5"

3. Get Current Camera Frame Detection:

bash
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict" -F "realtime=true"
# Or without file parameters
curl -X POST "http://127.0.0.1:8000/api/models/yolo11/predict"

Response Format (JSON):

json
{
  "success": true,
  "source": "video frame at 5.5s",
  "predictions": [
    {
      "class": "person",
      "confidence": 0.92,
      "box": { "x1": 100, "y1": 200, "x2": 300, "y2": 500 }
    }
  ],
  "image": { "width": 1280, "height": 720 }
}

2. System Configuration Interface (Config)

Used to dynamically adjust thresholds for real-time video streams and default inference.

Get Current Configuration

  • Endpoint: GET /api/config
  • Response: {"obj_thresh": 0.25, "nms_thresh": 0.45}

Update System Configuration

  • Endpoint: POST /api/config
  • Request Body (JSON): {"obj_thresh": 0.3, "nms_thresh": 0.5}
  • Response: {"status": "success"}

3. Command Line Arguments

web_detection.py supports the following arguments:

ArgumentDescriptionDefault
--model_pathPath to RKNN model file(Required)
--camera_idCamera device ID (e.g., fill 1 for /dev/video1)1
--video_pathPath to video file (overrides camera_id if provided)None
--class_pathPath to custom class configuration file (class_config.txt)None (Default COCO 80)
--hostWeb server listening address0.0.0.0
--portWeb server port8000

Custom Class Configuration (class_config.txt) Format:

Name classes with double quotes, separated by commas, for example: "person", "bicycle", "car", "motorbike"

Real-time Video Stream Interface (Video Feed)

Get real-time MJPEG video stream with detection boxes drawn, can be directly embedded in HTML <img> tags.

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