YOLOv8-seg Object Segmentation

• Enter the Radxa Model-Zoo YOLOv8_seg directory

  cd Radxa-Model-Zoo/sample/YOLOv8_seg

• Download the model, choose from F32/F16/INT8 quantized models

  # F32
  wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/yolov8s_fp32_1b.bmodel
  
  # F16
  wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/yolov8s_fp16_1b.bmodel
  
  # INT8
  wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/yolov8s_int8_1b.bmodel

• Download test images to the data folder

  mkdir images && cd images
  wget https://github.com/radxa-edge/TPU-Edge-AI/releases/download/model-zoo/dog_bike_car.jpg

• Create a virtual environment

  It's necessary to create a virtual environment, otherwise it may affect the normal operation of other applications, please refer to here for virtual environment usage

  python3 -m virtualenv .venv
  source .venv/bin/activate

• Install python dependencies

  pip3 install --upgrade pip
  pip3 install numpy pycocotools
  pip3 install https://github.com/radxa-edge/TPU-Edge-AI/releases/download/v0.1.0/sophon_arm-3.7.0-py3-none-any.whl

• Run python examples

  Under the python directory, there are a series of Python routines, as follows:

  No.	Python Routine	Description
  1	yolov8_opencv.py	Decode with OpenCV, preprocess with OpenCV, and infer with SAIL
  2	yolov8_bmcv.py	Decode with SAIL, preprocess with BMCV, and infer with SAIL

  • Run yolov8_opencv.py

    export LD_LIBRARY_PATH=/opt/sophon/libsophon-current/lib:$LD_LIBRARY_PATH
    export PYTHONPATH=$PYTHONPATH:/opt/sophon/sophon-opencv-latest/opencv-python/
    python3 python/yolov8_opencv.py --input ./images --bmodel ./yolov8s_int8_1b.bmodel

    Parameter explanation

    yolov8_opencv.py [--input IMG_PATH] [--bmodel BMODEL]

    --input: Inference image path, you can input the entire image folder path or video path

    --bmodel: Path of the bmodel used for inference

    (.venv) linaro@bm1684:/data/ssd/docs_check/Radxa-Model-Zoo/sample/YOLOv8_seg$ python3 python/yolov8_opencv.py --input ./images --bmodel ./yolov8s_int8_1b.bmodel
    [BMRT][bmcpu_setup:406] INFO:cpu_lib 'libcpuop.so' is loaded.
    bmcpu init: skip cpu_user_defined
    open usercpu.so, init user_cpu_init
    [BMRT][load_bmodel:1084] INFO:Loading bmodel from [./yolov8s_int8_1b.bmodel]. Thanks for your patience...
    [BMRT][load_bmodel:1023] INFO:pre net num: 0, load net num: 1
    INFO:root:load ./yolov8s_int8_1b.bmodel success!
    INFO:root:1, img_file: ./images/dog_bike_car.jpg
    sampleFactor=6, cinfo->num_components=3 (1x2, 1x1, 1x1)
    Open /dev/jpu successfully, device index = 0, jpu fd = 23, vpp fd = 24
    INFO:root:result saved in ./results/yolov8s_int8_1b.bmodel_images_opencv_python_result.json
    INFO:root:------------------ Predict Time Info ----------------------
    INFO:root:decode_time(ms): 16.52
    INFO:root:preprocess_time(ms): 30.85
    INFO:root:inference_time(ms): 16.11
    INFO:root:postprocess_time(ms): 71.09
    all done.

    The results will be saved in ./results/yolov8s_int8_1b.bmodel_images_opencv_python_result.json

    Image results are saved in ./result/images

    

  • Run yolov8_bmcv.py

    export LD_LIBRARY_PATH=/opt/sophon/libsophon-current/lib:$LD_LIBRARY_PATH
    export PYTHONPATH=$PYTHONPATH:/opt/sophon/sophon-opencv-latest/opencv-python/
    python3 python/yolov8_bmcv.py  --input ./images --bmodel ./yolov8s_int8_1b.bmodel

    Parameter explanation

    yolov8_bmcv.py [--input IMG_PATH] [--bmodel BMODEL]

    --input: Inference image path, you can input the entire image folder path or video path

    --bmodel: Path of the bmodel used for inference

    (.venv) linaro@bm1684:/data/ssd/docs_check/Radxa-Model-Zoo/sample/YOLOv8_seg$ python3 python/yolov8_bmcv.py  --input ./images --bmodel ./yolov8s_int8_1b.bmodel
    [BMRT][bmcpu_setup:406] INFO:cpu_lib 'libcpuop.so' is loaded.
    bmcpu init: skip cpu_user_defined
    open usercpu.so, init user_cpu_init
    [BMRT][load_bmodel:1084] INFO:Loading bmodel from [./yolov8s_int8_1b.bmodel]. Thanks for your patience...
    [BMRT][load_bmodel:1023] INFO:pre net num: 0, load net num: 1
    INFO:root:1, img_file: ./images/dog_bike_car.jpg
    sampleFactor=6, cinfo->num_components=3 (1x2, 1x1, 1x1)
    Open /dev/jpu successfully, device index = 0, jpu fd = 42, vpp fd = 43
    INFO:root:result saved in ./results/yolov8s_int8_1b.bmodel_images_bmcv_python_result.json
    INFO:root:------------------ Predict Time
    
    Info ----------------------
    INFO:root:decode_time(ms): 15.48
    INFO:root:preprocess_time(ms): 2.65
    INFO:root:inference_time(ms): 13.13
    INFO:root:postprocess_time(ms): 66.66
    all done.

    The results will be saved in ./results/yolov8s_int8_1b.bmodel_images_bmcv_python_result.json

    Image results are saved in ./result/images

    

FAQ

For more information on model conversion, model quantization details, and model accuracy testing, please refer to:

Model Conversion and Radxa Model-Zoo YOLOv8-seg