Project information
- ๐ฏ Objective:: The project aims to build a deep neural network model in Python that classifies traffic signs into different categories, enhancing the capabilities of autonomous vehicles to read and interpret road signs. This is crucial for achieving advanced levels of vehicle autonomy.
- date: Nov 2022 - Nov 2022
- Github Link: Github Link
Description:
๐ก Features:
- ๐๐๐๐ฉ ๐๐๐ฎ๐ซ๐๐ฅ ๐๐๐ญ๐ฐ๐จ๐ซ๐ค: Utilizes a simple Convolutional Neural Network (CNN) to classify images of traffic signs with a high degree of accuracy.
- ๐๐ข๐ ๐ก ๐๐๐๐ฎ๐ซ๐๐๐ฒ: The model successfully achieves 95% classification accuracy, indicating robust performance in recognizing and categorizing various traffic signs.
- ๐๐๐ซ๐ ๐ ๐๐ง๐ ๐๐ข๐ฏ๐๐ซ๐ฌ๐ ๐๐๐ญ๐๐ฌ๐๐ญ: Employs a dataset containing over 50,000 images spread across 43 different classes, offering a comprehensive range of traffic sign visuals for training the neural network.
- ๐๐ฒ๐ง๐๐ฆ๐ข๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง Includes visualization of accuracy and loss metrics over time during the training process, providing insights into the modelโs learning dynamics.
- ๐๐ฒ๐ญ๐ก๐จ๐ง ๐๐ง๐ ๐๐๐ ๐๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ฒ: Built using Python, leveraging the capabilities of CNN models for image classification tasks.
These features highlight the project's effectiveness in using deep learning to equip autonomous vehicles with the necessary tools to interpret traffic signs, a critical step towards fully autonomous driving.