Traffic Sign Classification

Description 📌

This project focuses on developing a deep learning model for traffic sign classification, with a primary goal of accurately identifying stop signs in images. Stop signs play a crucial role in traffic regulation and safety, and an automated system capable of detecting them in images can have significant applications in autonomous vehicles, traffic monitoring, and driver assistance systems.

Project Goal

The primary objective is to train a machine learning model capable of accurately classifying stop signs in images. Utilizing deep learning techniques, specifically convolutional neural networks (CNNs) and transfer learning, we aim to develop a robust model capable of achieving high accuracy in stop sign detection.

Techniques Used 🌟

• Transfer Learning: Utilizing pre-trained CNN models as a starting point for training on the specific task of traffic sign classification.
• Convolutional Neural Networks (CNNs): Effective for image recognition tasks, CNNs are used to capture spatial hierarchies of features in images.
• ResNet: Leveraging the depth and effectiveness of ResNet architecture for training deep neural networks, especially in tasks like traffic sign classification.

Dataset

The dataset used for training and testing the model is available here. It contains images of various traffic signs, organized into separate folders for different types of signs, including stop signs and non-stop signs.

Usage 📘

1. Requirements:

   • Ensure Python is installed.
   • Install dependencies from requirements.txt:

     pip install -r requirements.txt
     

2. Training the Model:

   • Open traffic_sign_classification.ipynb in Jupyter Notebook.
   • Follow instructions to train the model using the provided dataset.

3. Evaluation and Testing:

   • Evaluate the model's performance on the test dataset.
   • Use provided functions to make predictions on new images and visualize results.

4. Deployment:

   • Deploy the trained model in applications requiring traffic sign detection.

5. Optimizing Model Performance:

   • Enhance the model's accuracy by carefully balancing the dataset size with computational resources. Adding more images can improve accuracy and robustness, but it may also increase
     training time.