🚦 Road Sign Recognition Project 🖼️
This Python-based project presents a user-friendly graphical interface (GUI) facilitating image uploads for the identification of fundamental road signs typically encountered on streets. Recognizable signs include 20kmph, 30kmph, 50kmph, stop, and right turn.
The project is structured into two distinct files:
- Model Training: This section contains code for training the recognition model using labeled datasets, ensuring accurate sign identification.
- GUI Implementation: A graphical interface allowing users to upload images for immediate recognition using the pre-trained model.
Key Features:
- User-friendly GUI enabling effortless image uploads and swift sign recognition.
- Recognition of vital road signs: 20kmph, 30kmph, 50kmph, stop, and right turn.
- Pre-trained model achieving an accuracy rating of 0.9 out of 1.
- Modular separation of model training and GUI implementation for ease of use and maintenance.
Usage:
- Utilize the provided pre-trained model, boasting an accuracy of 0.9 out of 1.
- Leverage the GUI to upload images and receive instant sign recognition results.
This project serves as an educational resource to comprehend image recognition techniques applied specifically to road signs. It welcomes contributions, suggestions for enhancements, and usage for educational purposes.
Keywords: Python, Image Recognition, GUI, Road Signs, Machine Learning, TensorFlow, Pre-trained Model, GitHub.
Enjoy exploring and contributing! 🚗✨