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Introduction

Cvitek provides TDL integration algorithms to reduce the time required for application development.

This architecture realizes the algorithm required by TDL, including its pre and post processing, and provides a unified and convenient programming interface.

At present, TDL SDK includes motion detection, face detection, face recognition, face tracking, pedestrian detection, semantic segmentation, license plate recognition, license plate detection, live recognition, IR live recognition, infant detection, cry detection, attitude detection, gesture detection, Gesture Recognition and other algorithms.

Documents

Chinese Version(中文版)格式English VersionFormat
深度学习SDK软件开发指南htmlpdfTDL SDK Software Development Guidehtmlpdf
YOLO系列开发指南htmlpdfYOLO Development Guidehtmlpdf

Compilation

  1. Download toolchain

    wget https://sophon-file.sophon.cn/sophon-prod-s3/drive/23/03/07/16/host-tools.tar.gz
    tar xvf host-tools.tar.gz
    cd host-tools
    export PATH=$PATH:$(pwd)/gcc/riscv64-linux-musl-x86_64/bin
  2. Compile cvitek-tdl-sdk

    Duo:

    git clone https://github.com/milkv-duo/cvitek-tdl-sdk-cv180x.git
    cd cvitek-tdl-sdk-cv180x

    Duo256M and DuoS:

    git clone https://github.com/milkv-duo/cvitek-tdl-sdk-sg200x.git
    cd cvitek-tdl-sdk-sg200x

    Compile samples:

    cd sample
    ./compile_sample.sh

    The generated program is in the corresponding subdirectory in the sample directory. For example, the face detection example sample_vi_fd is located in

    cvi_tdl/sample_vi_fd

    Clean:

    ./compile_sample.sh clean

Example description

For detailed descriptions and running methods of each example, please refer to the following chapters.

https://developer.sophgo.com/thread/556.html

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