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OpenVINO-Deploy

OpenVINO-Deploy aims to implement popular AI models' inference deployment with OpenVINO written in C++, with a primary focus on object detection algorithms.

Test Environment

  1. OpenVINO 2023.3.0
  2. OpenCV 4.2.0
  3. Ubuntu 20.04

Models

Following models are implemented.

NameDescription
yolov3A fast and accurate real-time object detection algorithm that can detect multiple objects in an image or video stream with high efficiency.
yolov5A fast, accurate, and user-friendly real-time object detection algorithm.
yolov7A cutting-edge real-time object detection model delivering exceptional accuracy and speed.
yolov8An enhanced YOLO object detection model with improved performance and flexibility.
yolov9Paper yolov9 Implement, Learning What You Want to Learn Using Programmable Gradient Information.
rt-detrRT-DETR is a real-time object detection model that leverages transformers for efficient and accurate performance.
<!-- |[detr](./detr/)| A transformer-based object detection model that directly predicts object bounding boxes and classes.| -->

Quick Start

  1. Clone this repo
git clone https://github.com/wxxz975/OpenVINO-Deploy.git
  1. Create Build Directory
mkdir build && cd build
  1. Build All
cmake .. && make -j$(nproc)

Then, you will find executable files for demos in this build directory.

The onnx models exported by the experiment can be found on BaiduCloud

TODO List