Deploy YOLOv8 Object Detection on the Board

tip

This document demonstrates how to run YOLOv8 object detection inference on the RK3588 board. For the required environment setup, please refer to RKNN Installation.

This example uses a pre-trained ONNX model from the rknn_model_zoo to illustrate the complete process from model conversion on the PC to on-device inference. The target platform in this example is RK3588.

Deploying YOLOv8 with RKNN involves two main steps:

• On the PC, use rknn-toolkit2 to convert models from different frameworks into RKNN format.
• On the device, use the Python API of rknn-toolkit2-lite to run inference.

Model Conversion on PC

Radxa provides a pre-converted yolov8.rknn model. Users can skip the PC-side model conversion section and directly refer to YOLOv8 Inference on Device.

• If you are using Conda, first activate the rknn Conda environment:

  X86 Linux PC

  conda activate rknn

• Download the yolov8.onnx model:

  X86 Linux PC

  cd rknn_model_zoo/examples/yolov8/model
  # Download the pre-trained yolov8n.onnx model
  bash download_model.sh

  If you encounter network issues, you can visit this page to manually download the model and place it in the corresponding folder.

• Convert the ONNX model to yolov8n.rknn using rknn-toolkit2:

  X86 Linux PC

  cd rknn_model_zoo/examples/yolov8/python
  python3 convert.py ../model/yolov8n.onnx rk3588

  Parameter explanation:

  • <onnx_model>: Path to the ONNX model.
  • <TARGET_PLATFORM>: Name of the NPU platform. Options: rk3562, rk3566, rk3568, rk3576, rk3588, rk1808, rv1109, rv1126.
  • <dtype> (optional): Choose i8 or fp. i8 is for INT8 quantization; fp is for FP16 quantization. The default is i8.
  • <output_rknn_path> (optional): Path to save the RKNN model. By default it is saved in the same directory as the ONNX model with the filename yolov8.rknn.
  tip

  For RK358X users, set TARGET_PLATFORM to rk3588.

• Copy the yolov8n.rknn model to the device.

YOLOv8 Inference on Device

• Install the rknn-model-zoo-rk3588 package to obtain the YOLOv8 demo (which includes the pre-converted yolov8.rknn model):

  Radxa OS

   sudo apt install rknn-model-zoo-rk3588

  If you are using a CLI-only system, you can download the rknn-model-zoo-rk3588 deb package from the release page.

• Run the YOLOv8 example:

  If you are using a model converted on the PC, copy it to the device and specify the model path with the --model_path parameter.

  Radxa OS

  cd /usr/share/rknn_model_zoo/examples/yolov8/python
  sudo python3 yolov8.py --model_path ../model/yolov8.rknn --img_save

  Results

  $ sudo python3 yolov8.py --model_path ../model/yolov8.rknn --img_save
  import rknn failed,try to import rknnlite
  --> Init runtime environment
  I RKNN: [09:01:01.819] RKNN Runtime Information, librknnrt version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11)
  I RKNN: [09:01:01.819] RKNN Driver Information, version: 0.8.2
  W RKNN: [09:01:01.819] Current driver version: 0.8.2, recommend to upgrade the driver to the new version: >= 0.8.8
  I RKNN: [09:01:01.819] RKNN Model Information, version: 6, toolkit version: 1.6.0+81f21f4d(compiler version: 1.6.0 (585b3edcf@2023-12-11T07:56:14)), target: RKNPU v2, target platform: rk3588, framework name: ONNX, framework layout: NCHW, model inference type: static_shape
  W RKNN: [09:01:01.836] query RKNN_QUERY_INPUT_DYNAMIC_RANGE error, rknn model is static shape type, please export rknn with dynamic_shapes
  W Query dynamic range failed. Ret code: RKNN_ERR_MODEL_INVALID. (If it is a static shape RKNN model, please ignore the above warning message.)
  done
  Model-../model/yolov8.rknn is rknn model, starting val
  infer 1/1
  
  IMG: bus.jpg
  person @ (211 241 282 507) 0.872
  person @ (109 235 225 536) 0.860
  person @ (477 225 560 522) 0.856
  person @ (79 327 116 513) 0.306
  bus  @ (95 136 549 449) 0.860
  Detection result save to ./result/bus.jpg

  Parameter explanation:

  • --model_path: Path to the RKNN model.
  • --img_folder: Folder containing images for inference, default is ../model.
  • --img_save: Whether to save the inference result images to ./result. Default is False.

• All inference results are stored in the ./result directory.