| CNN | CV | GTSRB | Classification

Convolutional Neural Networks (CNN) with Computer Vision (CV) for GTSRB Traffic Sign Classification

This notebook delves into a classification task involving the use of Convolutional Neural Networks (CNNs) on the GTSRB dataset.

The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset comprising over 50,000 photos of road signs categorized into approximately 40 classes.

A detailed description of the dataset can be found at: http://benchmark.ini.rub.de/

We have structured the notebook into two main sections:

Objectives

This section delineates the specific goals of this notebook, which are:

• Training a Convolutional Neural Networks (CNNs) model to achieve high accuracy in classification of road signs.

Implementation

This section presents the hands-on steps necessary to attain the previously mentioned objectives. These steps include:

• Imports, Constants, and Methods: Setting up the necessary libraries, constants, and methods for our task.
• Data Retrieval: Acquiring the GTSRB dataset to be used for training and testing purposes.
• Data Preparation: Preprocessing and setting up the dataset to facilitate effective training of the CNN model.
• Model Creation: Architecting and constructing the CNN model utilizing Keras.
• Model Training: Engaging the CNN model in learning using the prepared dataset.
• Evaluation: Gauging the trained model's performance and analyzing the classification results.

Project:

• Github
• Kaggle