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Vitis AI Library v1.0


The Vitis AI Library is a set of high-level libraries and APIs built for efficient AI inference with Deep-Learning Processor Unit (DPU). It is built based on the Vitis AI Runtime with Unified APIs, and it fully supports XRT 2019.2.

The Vitis AI Library provides an easy-to-use and unified interface by encapsulating many efficient and high-quality neural networks. This simplifies the use of deep-learning neural networks, even for users without knowledge of deep-learning or FPGAs. The Vitis AI Library allows users to focus more on the development of their applications, rather than the underlying hardware.

Vitis AI Library directory structure introduction

├── demo                         # Application demo, including classification, yolov3,
|   |                            # seg_and_pose_detect and segs_roadline_detect
│   ├── classification
│   ├── seg_and_pose_detect
│   ├── segs_and_roadline_detect
│   └── yolov3
├── libsrc                       # AI library open source code
│   ├── libdpbase                # dpbase library using Vitis unified APIs
│   ├── libdpclassification
│   ├── libdpfacedetect
│   ├── libdpfacelandmark
│   ├── libdpmultitask
│   ├── libdpopenpose
│   ├── libdpposedetect
│   ├── libdprefinedet
│   ├── libdpreid
│   ├── libdproadline
│   ├── libdpsegmentation
│   ├── libdpssd
│   ├── libdptfssd               # Tensorflow SSD library
│   ├── libdpyolov2
│   └── libdpyolov3
├──                    # This README
└── samples                      # Model test samples, including jpeg test, video test, performance test
    ├── classification
    ├── facedetect
    ├── facelandmark
    ├── multitask
    ├── openpose
    ├── posedetect
    ├── refinedet
    ├── reid
    ├── roadline
    ├── segmentation
    ├── ssd
    ├── tfssd
    ├── yolov2
    └── yolov3

Quick Start

Setting Up the Host

  1. Download the vitis-ai-docker-runtime image

  2. Set up the docker runtime system according to the docker installation document.

  1. Copy or git clone the AI Library package to the workspace folder.

  2. Cross compile the demo in the AI Library, using yolov3 as example.

$cd /workspace/Vitis-AI/Vitis-AI-Library/demo/yolov3
$sh -x
  1. To compile the library sample in the AI Library, take classification for example, execute the following command.
$cd /workspace/Vitis-AI/Vitis-AI-Library/samples/classification
$sh -x
  1. To modify the library source code, view and modify them under /workspace/Vitis-AI/Vitis-AI-Library/libsrc. If you want to recompile the library, take libdpclassification for example, execute the following command:
$cd /workspace/Vitis-AI/Vitis-AI-Library/libsrc/libdpclassification
$sh -x

Setting Up the Target

  1. Installing a Board Image.

    • Download the SD card system image files from the following links:



      Note: The version of the board image should be 2019.2 or above.

    • Use Win32DiskImager (free opensource software) to burn the image file onto the SD card.

    • Insert the SD card with the image into the destination board.

    • Plug in the power and boot the board using the serial port to operate on the system.

    • Set up the IP information of the board using the serial port. You can now operate on the board using SSH.

  2. Installing AI Model Package

    • Download ZCU102 AI Model

      You can also download ZCU104 AI Model if you use ZCU104

    • Copy the downloaded file to the board using scp with the following command.

      $scp vitis_ai_model_ZCU102_2019.2-r1.0.deb root@IP_OF_BOARD:~/
    • Log in to the board (usong ssh or serial port) and install the model package.
    • Run the following command.
      #dpkg -i vitis_ai_model_ZCU102_2019.2-r1.0.deb
  3. Installing AI Library Package

    • Download the Vitis AI Library 1.0

    • Download the demo video files and untar into the corresponding directories.

    • Download the demo image files and untar into the corresponding directories.

    • Copy the downloaded file to the board using scp with the following command.

      $scp vitis_ai_library_2019.2-r1.0.deb root@IP_OF_BOARD:~/
    • Log in to the board using ssh. You can also use the serial port to login.
    • Install the Vitis AI Library.
      #dpkg -i vitis_ai_library_2019.2-r1.0.deb

Running Vitis AI Library Examples

  1. Enter the extracted directory of example in target board.

    #cd /usr/share/vitis_ai_library/samples/facedetect
  2. Run the image example.

    #./test_jpeg_facedetect densebox_640_360 sample_facedetect.jpg
  3. Run the video example.

	#./test_video_facedetect densebox_640_360 video_input.mp4 -t 8
	Video_input.mp4: The video file's name for input.The user needs to prepare the videofile.
	-t: <num_of_threads>
  1. To test the program with a USB camera as input, run the following command:
	#./test_video_facedetect densebox_640_360 0 -t 8
	0: The first USB camera device node. If you have multiple USB camera, the value might be 1,2,3 etc.
	-t: <num_of_threads>
  1. To test the performance of model, run the following command:
	#./test_performance_facedetect densebox_640_360 test_performance_facedetect.list -t 8 -s 60
	-t: <num_of_threads>
	-s: <num_of_seconds>
  1. To check the version of Vitis AI Library, run the following command:
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