CV / ResNet

ResNet50V2

ResNet50V2 image classification accelerated by RKNN on reComputer RK3576 and RK3588.

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-resnet50v2:latest \
  python3 web_classification.py --model_path model/rk3576_resnet50-v2-7.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": "resnet50-v2-rknn",
  "messages": [{"role": "user", "content": "Hello"}]
}'

Python

import requests

resp = requests.post(
    "http://localhost:8080/v1/chat/completions",
    json={"model": "resnet50-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 ResNet50V2 READMEs in reComputer-RK-CV. The service runs ImageNet classification on the Rockchip NPU and provides browser, MJPEG, health-check, and REST interfaces.

Model Information

Property	Value
Task	Image classification
Input	`224 x 224` RGB image
Output	Configurable Top-K ImageNet labels and confidence scores
RK3576 model	`model/rk3576_resnet50-v2-7.rknn`
RK3588 model	`model/rk3588_resnet50-v2-7.rknn`
Labels	`model/synset.txt`

Step A: Pull Images

bash

sudo docker pull ghcr.io/seeed-projects/recomputer-rk-cv/rk3588-resnet50v2:latest
sudo docker pull ghcr.io/seeed-projects/recomputer-rk-cv/rk3576-resnet50v2: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-resnet50v2:latest \
  python3 web_classification.py \
  --model_path model/rk3576_resnet50-v2-7.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-resnet50v2:latest \
  python3 web_classification.py \
  --model_path model/rk3588_resnet50-v2-7.rknn --camera_id -1

Open http://<BOARD_IP>:8000 for the Web page or http://<BOARD_IP>:8000/docs for OpenAPI.

🔌 API Documentation

1. Model Inference Interface (Predict)

Endpoint: POST /api/models/resnet50v2/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/resnet50v2/predict" \
  -F "file=@dog_224x224.jpg" -F "topk=5" -F "conf=0.01"

bash

curl -X POST "http://<BOARD_IP>:8000/api/models/resnet50v2/predict" \
  -F "video=@test.mp4" -F "timestamp=5.5" -F "topk=3"

Response Format (JSON):

json

{
  "success": true,
  "model": "resnet50v2",
  "source": "uploaded image",
  "predictions": [
    {"class": "n02099601 golden retriever", "confidence": 0.89}
  ],
  "image": {"width": 224, "height": 224}
}

2. System Configuration Interface (Config)

GET /api/config returns conf_thresh; update it with JSON through POST /api/config. Request-level conf takes precedence.

Endpoint	Purpose
`GET /api/health`	Service, platform, model, and readiness.
`GET /api/video_feed`	Annotated MJPEG preview.
`POST /api/video/upload`	Upload an image or MP4.
`POST /api/video/analyze`	Analyze an uploaded `filename`.
`GET /api/video/status`	Processing progress and errors.
`GET /api/video/list`	Uploaded and generated files.
`GET /api/video/download/{filename}`	Download a result.

3. Command Line Arguments

The application accepts an uploaded image, a selected frame from an uploaded MP4, a looping local video, or a camera frame.

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; it overrides the camera.
`--host`	`0.0.0.0`	FastAPI listen address.
`--port`	`8000`	Service port.

Map /dev/videoN into the container when selecting camera N.

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 runtime resizes input to 224 x 224, converts BGR to RGB, applies softmax, and filters Top-K results. A replacement model must preserve the expected input layout, normalization, channel order, and output format. Update synset.txt if the class set changes. Runtime files are stored under workspace/; set RK_CV_WORKSPACE to override that location.

Build Local Images

bash

docker build -f docker/rk3576/resnet50v2.dockerfile \
  -t rk3576-resnet50v2:local src/rk3576_resnet50v2

docker build -f docker/rk3588/resnet50v2.dockerfile \
  -t rk3588-resnet50v2:local src/rk3588_resnet50v2

Inputs and Outputs

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