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YOLO-Deploy-QT_Interface

Visual QT interface for deploying YOLOv5 and YOLOv8 for ONNX and OpenVino deployment of images, folders, videos, cameras demo

1.Environment Setup

This program has been verified to work on both Windows 10 and Ubuntu 20.04 systems. This program uses Conda to create an environment, which is created and activated by following the following commands:

conda create -n qtenv python=3.8
conda activate qtenv

After activating the environment, go to this project folder and install the necessary packages for the environment:

pip install -r requirements.txt

At this point, the environment configuration is complete and can be used as the environment for the project.

2.Data and model preparation

  • Before using the software, you need to prepare the images, folders, videos, cameras, etc. that need to be detected
  • Download this project provides YOLOv5, YOLOv8 ONNX, OpenVINO model file(note Unzip) and put it in the project folder
  • Note: The model file provided in this project is directly converted from the YOLOv5/8 COCO pre-training model. If you need to customize the categories, you need to change to your own model and modify the categories in line 12 and line 32 of deploy_yolov5.py and deploy_yolov8.py

3. User guide

  • Open and run main.py
  • Select Model Type, Deployment Type, File Type, Model Location, and File Location, and set parameters
  • Click "Save configuration", then click "Start running" to run, you can see the program running status in "Output log"
  • "Stop Running" can stop the program running, "exit" can exit the program
  • Program results are stored in the./results folder

4. Statement

  • If the code is helpful to you, please click a Star, you can raise Issues together
  • Please indicate the source of the code, refuse white whoring, carry forward the open source spirit together, piracy will be prosecuted!