Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI
This YOLOv5🚀😊 GUI road sign system uses MySQL💽, PyQt5🎨, PyTorch, CSS🌈. It has modules for login🔑, YOLOv5 setup📋, sign recognition🔍, database💾, and image processing🖼️. It supports diverse inputs, model switching, and enhancements like mosaic and mixup📈.
https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI
css mysql-database pyqt5 pytorch tensorrt-engine yolov5 yolov5-gui
Last synced: 4 days ago
JSON representation
This YOLOv5🚀😊 GUI road sign system uses MySQL💽, PyQt5🎨, PyTorch, CSS🌈. It has modules for login🔑, YOLOv5 setup📋, sign recognition🔍, database💾, and image processing🖼️. It supports diverse inputs, model switching, and enhancements like mosaic and mixup📈.
- Host: GitHub
- URL: https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI
- Owner: Ai-trainee
- License: gpl-3.0
- Created: 2023-04-13T10:23:48.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-21T08:03:20.000Z (6 months ago)
- Last Synced: 2024-11-05T06:51:45.612Z (9 days ago)
- Topics: css, mysql-database, pyqt5, pytorch, tensorrt-engine, yolov5, yolov5-gui
- Language: Python
- Homepage: https://ai-trainee.github.io/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI/
- Size: 107 MB
- Stars: 176
- Watchers: 2
- Forks: 32
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI - trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI?style=social"/> : Road Sign Recognition Project Based on YOLOv5. This is a road sign recognition project based on YOLOv5, developed with a PyQt5 interface, YOLOv5 trained model, and MySQL database. 这是一个基于YOLOv5🚀的道路标志识别系统😊,使用了MySQL数据库💽,PyQt5进行界面设计🎨,PyTorch深度学习框架和TensorRT进行加速⚡,同时包含了CSS样式🌈。系统由五个主要模块组成:系统登录模块🔑负责用户登陆;初始化参数模块📋提供YOLOv5模型的初始化参数设置;标志识别模块🔍是系统的核心,负责对道路标志进行识别并将结果导入数据库;数据库模块💾包含基本数据库操作和数据分析两个子模块;图像处理模块🖼️负责单个图像的处理和数据增强。整个系统支持多种数据输入和模型切换,提供了包括mossic和mixup在内的图像增强方法📈。 (Applications)