Developing with User Model and AI Library API_2 - 1.4.1 English

Vitis AI Library User Guide (UG1354)

Document ID
Release Date
1.4.1 English
When you use your own models, it is important to note that your model framework should be within the scope supported by the Vitis AI library. The following is an introduction of how to deploy a retrained YOLOv3 Caffe model to ZCU102 platform based on Vitis AI library step by step.
  1. Download the corresponding docker image from
  2. Load and run the docker.
  3. Create a folder and place the float model under it on the host side, then use AI Quantizer tool to do the quantization. For more details, see Vitis AI User Guide in the Vitis AI User Documentation (UG1431).
  4. Use AI Compiler tool to do the model compiling to get the xmodel file, such as yolov3_custom.xmodel. For more information, see Vitis AI User Guide in the Vitis AI User Documentation (UG1431).
  5. Create the yolov3_custom.prototxt, as shown in the following.
    model {
      name: "yolov3_custom"
      kernel {
         name: "yolov3_custom"
         mean: 0.0
         mean: 0.0
         mean: 0.0
         scale: 0.00390625
         scale: 0.00390625
         scale: 0.00390625
      model_type : YOLOv3
      yolo_v3_param {
        num_classes: 20
        anchorCnt: 3
        layer_name: "59"
        layer_name: "67"
        layer_name: "75"
        conf_threshold: 0.3
        nms_threshold: 0.45
        biases: 10
        biases: 13
        biases: 16
        biases: 30
        biases: 33
        biases: 23
        biases: 30
        biases: 61
        biases: 62
        biases: 45
        biases: 59
        biases: 119
        biases: 116
        biases: 90
        biases: 156
        biases: 198
        biases: 373
        biases: 326
        test_mAP: false
    Note: The <model_name>.prototxt is effective only when you use AI Library API_1.

    When you use AI Library API_2, the parameter of the model needs to be loaded and read manually by the program. See the Vitis-AI/demo/Vitis-AI-Library/samples/dpu_task/yolov3/demo_yolov3.cpp for details.

  6. Create the demo_yolov3.cpp file. See Vitis-AI/demo/Vitis-AI-Library/samples/dpu_task/yolov3/demo_yolov3.cpp for reference.
  7. Create a file as shown below, or copy one from the Vitis AI library demo and modify it.
    $CXX -std=c++17 -O3 -I. -o demo_yolov3 demo_yolov3.cpp -lopencv_core -lopencv_video -lopencv_videoio -lopencv_imgproc -lopencv_imgcodecs -lopencv_highgui -lglog -lxnnpp-xnnpp -lvitis_ai_library-model_config -lprotobuf -lvitis_ai_library-dpu_task
  8. Exit the docker tool system and start the docker runtime system.
  9. Cross compile the program and generate executable file demo_yolov3.
    sh -x
  10. Create model folder under /usr/share/vitis_ai_library/models on the target side.
    mkdir yolov3_custom /usr/share/vitis_ai_library/models
    Note: /usr/share/vitis_ai_library/models is the default location for the program to read the model. You can also place the model folder in the same directory as the executable program.
  11. Copy the yolov3_custom.xmodel and yolov3_custom.prototxt to the target and put them under /usr/share/vitis_ai_library/models/yolov3_custom.
    scp yolov3_custom.xmodel  yolov3_custom.prototxt root@IP_OF_BOARD:/usr/share/vitis_ai_library/models/yolov3_custom
  12. Copy the executable program to the target board using scp.
    scp demo_yolov3 root@IP_OF_BOARD:~/
  13. Execute the program on the target board and get the following results. Before running the program, make sure the target board has the Vitis AI library installed, and prepare the images you want to test.
    ./demo_yolov3 yolov3_custom sample.jpg