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YOLOv8n_seg

YOLOv8n-Seg Instance Segmentation Series lightweight version, compact and efficient, designed for pixel-level object segmentation and mask prediction, ideal for edge deployment.

69 downloads
Size
~7.2MB
Memory
1GB+
Precision
INT8

Choose the device you're using, the set up guide and documentation will update accordingly.

Getting Started

Docker
sudo docker run -it --rm --pull always --runtime=nvidia \
  --network host ghcr.io/seeed-studio/vllm:latest-rk3588 \
  vllm serve yolov8n_seg

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/yolov8_seg/predict" -F "realtime=true"
# Or without file parameters
curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/predict"
Python
import requests
import json
resp = requests.post(
    "http://127.0.0.1:8000/api/models/yolov8_seg/predict",
    json={},
    timeout=30
)
result = resp.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 simultaneous preview via Local GUI and Web Browser. The program automatically detects the display environment and downgrades to Web mode if no display is connected.

Step A: Configure Display Permissions (Optional)

If you have a monitor connected and want to see the window locally:

bash
xhost +local:docker

Step B: One-click Run

For RK3588:

bash
sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    --device /dev/video0:/dev/video0 \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/seeed-projects/recomputer-rk-cv/rk3588-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n_seg.rknn --video video/test.mp4

For RK3576:

bash
sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    --device /dev/video0:/dev/video0 \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n_seg.rknn --video video/test.mp4

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/video0:/dev/video0 \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n-seg.rknn --video video/test.mp4 --class_path class_config.txt

Note: If you want to test with a local video instead of a camera, use the --video_path parameter:

bash
sudo docker run --rm --privileged --net=host \
    -e PYTHONUNBUFFERED=1 \
    -e RKNN_LOG_LEVEL=0 \
    -v $(pwd)/video:/app/video \
    --device /dev/dri/renderD128:/dev/dri/renderD128 \
    -v /proc/device-tree/compatible:/proc/device-tree/compatible \
    ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-yolov8_seg:latest \
    python3 web_detection.py --model_path model/yolov8n-seg.rknn --video_path video/test.mp4

🔌 API Documentation

This project provides RESTful interfaces compatible with the Ultralytics Cloud API standard, supporting instance segmentation via image, video uploads or direct camera calls.

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/yolov8_seg/predict (or /api/models/yolo11/predict depending on exact script mapping)

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/yolov8_seg/predict" -F "file=@/home/cat/001.jpg"

2. Video Specific Frame Detection:

bash
curl -X POST "http://127.0.0.1:8000/api/models/yolov8_seg/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/yolov8_seg/predict" -F "realtime=true"

Response Format (JSON):

json
{
  "success": true,
  "source": "realtime camera frame",
  "predictions": [
    {
      "class": "cup",
      "confidence": 0.9840346574783325,
      "box": { "x1": 100, "y1": 200, "x2": 300, "y2": 500 },
      "polygons": [
        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      ]
    }
  ],
  "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. Real-time Video Stream Interface (Video Feed)

Get real-time MJPEG video stream with detection boxes and segmentation masks 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">

🛠️ Developer Guide (Production Recommendations)

Code Description

  • web_detection.py:
    • Dual-mode Support: Integrates FastAPI, supporting both local rendering and MJPEG streaming output.
    • Environment Adaptive: Automatically detects the DISPLAY environment variable, silently skipping GUI initialization if not present.
    • RKNN Inference: Encapsulates RKNN initialization, model loading, and multi-core inference logic.
    • Dynamic Loading: Supports dynamic class configuration loading via --class_path.
    • Post-processing: High-performance Numpy-based bounding box decoding, NMS, and contour extraction (cv2.findContours).

Modifying Models

  1. Place the trained and converted .rknn model into the model/ directory.
  2. Add the --model_path argument to the running command to point to the new model.