CV / MobileNet

MobileNetV2

MobileNetV2 image classification accelerated by RKNN on reComputer RK3576 and RK3588.

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Precision

RKNN

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 \
  -e PYTHONUNBUFFERED=1 -e RKNN_LOG_LEVEL=0 \
  --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-mobilenet:latest \
  python3 web_classification.py --model_path model/rk3576_mobilenet_v2.rknn --camera_id -1

REST API

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

Curl

curl http://localhost:8080/v1/chat/completions -d '{
  "model": "mobilenet-v2-rknn",
  "messages": [{"role": "user", "content": "Hello"}]
}'

Python

import requests

resp = requests.post(
    "http://localhost:8080/v1/chat/completions",
    json={"model": "mobilenet-v2-rknn", "messages": [{"role": "user", "content": "Hello"}]},
)
print(resp.json())

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 page is adapted from the MobileNetV2 READMEs in reComputer-RK-CV. The service runs ImageNet classification with RKNN-Toolkit-Lite2 and provides a Web preview, MJPEG stream, health check, and REST API.

Model Information

Property	Value
Task	Image classification
Input	`224 x 224` image
Output	Configurable Top-K ImageNet labels and confidence scores
RK3576 model	`model/rk3576_mobilenet_v2.rknn`
RK3588 model	`model/rk3588_mobilenet_v2.rknn`
Labels	`model/synset.txt`

Step A: Pull Images

bash

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

Step B: Run with One Click

For RK3576:

bash

sudo docker run --rm --privileged --net=host \
  -e PYTHONUNBUFFERED=1 -e RKNN_LOG_LEVEL=0 \
  --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-mobilenet:latest \
  python3 web_classification.py \
  --model_path model/rk3576_mobilenet_v2.rknn --camera_id -1

For RK3588:

bash

sudo docker run --rm --privileged --net=host \
  -e PYTHONUNBUFFERED=1 -e RKNN_LOG_LEVEL=0 \
  --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-mobilenet:latest \
  python3 web_classification.py \
  --model_path model/rk3588_mobilenet_v2.rknn --camera_id -1

Open http://<BOARD_IP>:8000 for the Web page or http://<BOARD_IP>:8000/docs for OpenAPI. Replace --camera_id -1 with a mapped camera ID, or use --video_path video/test.mp4 for the bundled video.

🔌 API Documentation

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/mobilenet/predict

Request Parameters (Multipart/Form-Data):

file: Optional image file to classify.
video: Optional MP4 file; it cannot be supplied together with file.
timestamp: Optional non-negative video timestamp in seconds; defaults to the first frame.
conf: Optional request-level confidence threshold from 0.0 to 1.0.
topk: Optional number of results from 1 to 20; defaults to 5.
With neither file nor video, the current camera or local-video frame is used when available.

Usage Examples:

bash

curl -X POST "http://<BOARD_IP>:8000/api/models/mobilenet/predict" \
  -F "file=@bell.jpg" -F "topk=5" -F "conf=0.01"

To classify a frame at 5.5 seconds in an MP4:

bash

curl -X POST "http://<BOARD_IP>:8000/api/models/mobilenet/predict" \
  -F "video=@test.mp4" -F "timestamp=5.5" -F "topk=3"

Response Format (JSON):

json

{
  "success": true,
  "model": "mobilenet",
  "source": "uploaded image",
  "predictions": [
    {"class": "n02123045 tabby, tabby cat", "confidence": 0.91}
  ],
  "image": {"width": 640, "height": 480}
}

2. System Configuration Interface (Config)

GET /api/config returns the global conf_thresh. Update it with POST /api/config and JSON such as {"conf_thresh":0.1}. A request-level conf value overrides the global threshold for that request.

Endpoint	Purpose
`GET /api/health`	Platform, model path, and readiness.
`GET /api/video_feed`	Annotated MJPEG preview.
`POST /api/video/upload`	Upload JPG, JPEG, PNG, BMP, or MP4.
`POST /api/video/analyze`	Start asynchronous analysis by uploaded `filename`.
`GET /api/video/status`	Progress and error state.
`GET /api/video/list`	Uploaded and generated files.
`GET /api/video/download/{filename}`	Download a generated result.

3. Command Line Arguments

The application accepts an uploaded image, an uploaded MP4 frame selected by timestamp, a looping local video, or a camera frame. A local video takes precedence over the camera.

Argument	Default	Description
`--model_path`	Required	RKNN model selected for the target SoC.
`--camera_id`	`1`	Camera index; use `-1` for upload-only mode.
`--video_path`	None	Local MP4 path. Overrides `--camera_id`.
`--host`	`0.0.0.0`	FastAPI listen address.
`--port`	`8000`	Service port.

For a USB camera, map the matching device node, for example --device /dev/video0:/dev/video0, and pass --camera_id 0.

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)

Model Replacement

The preprocessing keeps the BGR channel order required by this model, applies softmax, and filters the Top-K results. A replacement model must preserve the input layout, channel order, normalization, and output format. Update synset.txt when changing the class set. Runtime uploads and outputs are stored under workspace/; set RK_CV_WORKSPACE to use a different directory.

Build Local Images

Run from the upstream repository root:

bash

docker build -f docker/rk3576/mobilenet.dockerfile \
  -t rk3576-mobilenet:local src/rk3576_mobilenet

docker build -f docker/rk3588/mobilenet.dockerfile \
  -t rk3588-mobilenet:local src/rk3588_mobilenet

Inputs and Outputs

Input: image, video frame, or camera frame. Output: Top-K ImageNet labels and confidence scores.