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RKNN MODEL ZOO Application Examples

RKNN MODEL ZOO is a collection of deployment examples for various mainstream algorithms supported by RKNPU, provided by Rockchip officially. The latest examples support mobilenet model deployment and yolo model deployment. This chapter introduces the usage of RKNN MODEL ZOO examples using yolov10 deployment as an example.

1 Export RKNN Model​

  1. Download RKNN MODEL ZOO

    git clone https://github.com/airockchip/rknn_model_zoo.git

  2. Get Yolov10 ONNX Model File

    cd <rknn_model_zoo Path>/rknn_model_zoo/examples/yolov10/model
    chmod a+x download_model.sh
    ./download_model.sh

  3. Execute Model Conversion Program

    conda activate RKNN-Toolkit2
    cd <rknn_model_zoo Path>/rknn_model_zoo/examples/yolov10/python
    python3 convert.py ../model/yolov10n.onnx rk3576
    # output model will be saved as ../model/yolov10.rknn

    Parameter Description:

    python3 convert.py <onnx_model> <TARGET_PLATFORM> <dtype(optional)> <output_rknn_path(optional)>

    ◦ <onnx_model>: ONNX model path ◦ <TARGET_PLATFORM>: Specify NPU platform name (e.g., rk3576) ◦ <quant_dtype>: Quantization type (i8/fp, default is i8) ◦ <output_rknn_path>: Output path (default saves as yolov10n.rknn)

2 Compile and Build​

  1. Set Cross-compilation Environment Variables

    export GCC_COMPILER=/home/xt/Luckfox/omni3576/sdk-1026/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu

  2. Execute Compilation Script

    chmod +x ./build-linux.sh
    ./build-linux.sh -t rk3576 -a aarch64 -d yolov10

    Compilation Process Example:

    (RKNN-Toolkit2) user@user:~/rknn_model_zoo$ ./build-linux.sh -t rk3576 -a aarch64 -d yolov10
    ./build-linux.sh -t rk3576 -a aarch64 -d yolov10
    /home/xt/Luckfox/omni3576/sdk-1026/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu
    ===================================
    BUILD_DEMO_NAME=yolov10
    BUILD_DEMO_PATH=examples/yolov10/cpp
    TARGET_SOC=rk3576
    TARGET_ARCH=aarch64
    BUILD_TYPE=Release
    ENABLE_ASAN=OFF
    DISABLE_RGA=OFF
    INSTALL_DIR=/home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo
    BUILD_DIR=/home/xt/conda/rknn_model_zoo/build/build_rknn_yolov10_demo_rk3576_linux_aarch64_Release
    CC=/home/xt/Luckfox/omni3576/sdk-1026/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu-gcc
    CXX=/home/xt/Luckfox/omni3576/sdk-1026/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu-g++
    ===================================
    -- The C compiler identification is GNU 10.3.1
    -- The CXX compiler identification is GNU 10.3.1
    -- Detecting C compiler ABI info
    -- Detecting C compiler ABI info - done
    -- Check for working C compiler: /home/xt/Luckfox/omni3576/sdk-1026/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu-gcc - skipped
    -- Detecting C compile features
    -- Detecting C compile features - done
    -- Detecting CXX compiler ABI info
    -- Detecting CXX compiler ABI info - done
    -- Check for working CXX compiler: /home/xt/Luckfox/omni3576/sdk-1026/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu-g++ - skipped
    -- Detecting CXX compile features
    -- Detecting CXX compile features - done
    -- !!!!!!!!!!!CMAKE_SYSTEM_NAME: Linux
    -- Performing Test CMAKE_HAVE_LIBC_PTHREAD
    -- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
    -- Check if compiler accepts -pthread
    -- Check if compiler accepts -pthread - yes
    -- Found Threads: TRUE  
    -- Configuring done (0.4s)
    -- Generating done (0.0s)
    -- Build files have been written to: /home/xt/conda/rknn_model_zoo/build/build_rknn_yolov10_demo_rk3576_linux_aarch64_Release
    [ 33%] Building C object utils.out/CMakeFiles/imageutils.dir/image_utils.c.o
    [ 33%] Building C object utils.out/CMakeFiles/fileutils.dir/file_utils.c.o
    [ 33%] Building C object utils.out/CMakeFiles/audioutils.dir/audio_utils.c.o
    [ 33%] Building C object utils.out/CMakeFiles/imagedrawing.dir/image_drawing.c.o
    [ 41%] Linking C static library libaudioutils.a
    ...
    ...
    ...
    [100%] Linking CXX executable rknn_yolov10_demo
    [100%] Built target rknn_yolov10_demo
    [ 16%] Built target imageutils
    [ 33%] Built target fileutils
    [ 50%] Built target imagedrawing
    [ 83%] Built target rknn_yolov10_demo
    [100%] Built target audioutils
    Install the project...
    -- Install configuration: "Release"
    -- Installing: /home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/./rknn_yolov10_demo
    -- Set non-toolchain portion of runtime path of "/home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/./rknn_yolov10_demo" to "$ORIGIN/../lib"
    -- Installing: /home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/model/bus.jpg
    -- Installing: /home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/model/coco_80_labels_list.txt
    -- Installing: /home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/model/yolov10.rknn
    -- Installing: /home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/lib/librknnrt.so
    -- Installing: /home/xt/conda/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov10_demo/lib/librga.so

  3. Output Results ◦ Generated executable file: install/rk3576_linux_aarch64/rknn_yolov10_demo ◦ Dependencies: model files, test images, dynamic link libraries, etc.

Note:

  • Ensure the cross-compilation toolchain path is correct during compilation
  • If permission issues occur, execute chmod +x to grant script execution rights
  • The final deployment package contains all files needed for execution and can be directly copied to the development board

3 Run the Program​

  1. First, transfer the entire rknn_yolov10_demo directory to the development board, then execute the following command to run the program:
scp -r rknn_yolov10_demo/ user@192.168.253.105:/home/user
  1. After inference is complete, an image out.png will be generated
user@user:~/rknn_yolov10_demo$ ./rknn_yolov10_demo ./model/yolov10.rknn ./model/bus.jpg 
load lable ./model/coco_80_labels_list.txt
model input num: 1, output num: 6
input tensors:
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
output tensors:
  index=0, name=487, n_dims=4, dims=[1, 64, 80, 80], n_elems=409600, size=409600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-38, scale=0.114574
  index=1, name=501, n_dims=4, dims=[1, 80, 80, 80], n_elems=512000, size=512000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.002001
  index=2, name=508, n_dims=4, dims=[1, 64, 40, 40], n_elems=102400, size=102400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-57, scale=0.095044
  index=3, name=522, n_dims=4, dims=[1, 80, 40, 40], n_elems=128000, size=128000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003505
  index=4, name=529, n_dims=4, dims=[1, 64, 20, 20], n_elems=25600, size=25600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-58, scale=0.061253
  index=5, name=543, n_dims=4, dims=[1, 80, 20, 20], n_elems=32000, size=32000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003792
model is NHWC input fmt
model input height=640, width=640, channel=3
origin size=640x640 crop size=640x640
input image: 640 x 640, subsampling: 4:2:0, colorspace: YCbCr, orientation: 1
scale=1.000000 dst_box=(0 0 639 639) allow_slight_change=1 _left_offset=0 _top_offset=0 padding_w=0 padding_h=0
rga_api version 1.10.1_[0]
rknn_run
bus @ (88 137 556 438) 0.925
person @ (109 234 225 536) 0.910
person @ (210 240 284 511) 0.906
person @ (478 233 560 519) 0.796
person @ (80 330 114 518) 0.428
write_image path: out.png width=640 height=640 channel=3 data=0x7fbdd5c010


user@user:~/rknn_yolov10_demo$ ls
lib  model  out.png  rknn_yolov10_demo
  1. You can download images according to the labels yourself. The label file is located at rknn_yolov10_demo/model/coco_80_labels_list.txt. For example, the object detector can locate office supplies in the image.
./rknn_yolov10_demo ./model/yolov10.rknn ./model/laptop.jpg

Inference result: alt text