CV / YOLOX

YOLOX

YOLOX COCO object detection on reComputer RK3576, RK3588, and CM5 with Hailo-8 or Hailo-10H.

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Precision

RKNN / Hailo HEF

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

Platform

Size

Inference Engine

Getting Started

Deploy

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-yolox:latest \
  python3 web_detection.py --platform rk3576 --model_path model/yolox_s.rknn \
  --class_path model/coco_80_labels_list.txt --video_path video/test.mp4

REST API

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

Curl

curl http://localhost:8080/v1/chat/completions -d '{
  "model": "yolox-rknn",
  "messages": [{"role": "user", "content": "Hello"}]
}'

Python

import requests

resp = requests.post(
    "http://localhost:8080/v1/chat/completions",
    json={"model": "yolox-rknn", "messages": [{"role": "user", "content": "Hello"}]},
)
print(resp.json())

Model Details

reComputer RK

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 page is based on the YOLOX services in reComputer-RK-CV. The runtime performs RKNN inference, YOLOX branch decoding, objectness/class-score fusion, and class-aware NMS.

Model Information

Property	Value
Task	COCO 80-class object detection
Input	`640 x 640` letterboxed RGB
Variants	`yolox_s.rknn`, `yolox_m.rknn`
Default	`model/yolox_s.rknn`
Labels	`model/coco_80_labels_list.txt`

Step A: Pull Images

bash

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

Step B: Run with One Click

For RK3576:

bash

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-yolox:latest \
  python3 web_detection.py --platform rk3576 \
  --model_path model/yolox_s.rknn \
  --class_path model/coco_80_labels_list.txt --video_path video/test.mp4

For RK3588:

bash

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-yolox:latest \
  python3 web_detection.py --platform rk3588 \
  --model_path model/yolox_s.rknn \
  --class_path model/coco_80_labels_list.txt --video_path video/test.mp4

Use model/yolox_m.rknn for the medium variant. Keep the class-path argument when switching models. Open http://<BOARD_IP>:8000 or /docs.

🔌 API Documentation

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/yolox/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: Optional request-level NMS IoU threshold; defaults to 0.45.

Usage Examples:

bash

curl -X POST "http://<BOARD_IP>:8000/api/models/yolox/predict" \
  -F "file=@bus.jpg" -F "conf=0.25" -F "iou=0.45"

Response Format (JSON):

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}
}

The service accepts image, uploaded-video frame, and current-source requests.

2. System Configuration Interface (Config)

GET/POST /api/config controls obj_thresh and nms_thresh; request-level conf and iou override them. Health, MJPEG, video upload, asynchronous analysis, status, list, and download endpoints are also available.

Input is letterboxed, converted to RGB, decoded by branch, and processed with objectness/class-score fusion and class-aware NMS. Replacement models must match this included YOLOX decoder and class configuration.

3. Command Line Arguments

Argument	Default	Description
`--platform`	Required	`rk3576` or `rk3588`.
`--model_path`	Required	YOLOX-S or YOLOX-M RKNN file.
`--class_path`	Required by the packaged command	COCO class-name file.
`--camera_id`	`1`	Camera index; `-1` enables uploads only.
`--video_path`	None	Looping local MP4; overrides the camera.
`--host` / `--port`	`0.0.0.0` / `8000`	Service address and port.

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

bash

docker build -f docker/rk3576/yolox.dockerfile \
  -t rk3576-yolox:local src/rk3576_yolox

docker build -f docker/rk3588/yolox.dockerfile \
  -t rk3588-yolox:local src/rk3588_yolox

Inputs and Outputs

Input: image, video frame, or camera frame. Output: COCO classes, confidence scores, and boxes.