Project - Traffic Sign Classification Using Deep Learning in Python/Keras
Dataset
Objectives
- Understand the theory and intuition behind Deep Learning and Convolutional Neural Networks (CNNs).
- Import Key python libraries, dataset and perform image visualization.
- Perform image normalization and convert images from color-scaled to gray-scaled.
- Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a back-end.
- Compile and fit Deep Convolutional Neural Network model to training data.
- Assess the performance of trained Convolutional Neural Network model and ensure its generalization using various KPIs
Classify Traffic Sign Using Deep Learning for Self-Driving Cars is divided into following tasks
- Task 1: overview
- Task 2: Import Libraries and data-sets
- Task 3: Perform image visualization
- Task 4: Convert images to gray-scale and perform normalization
- Task 5: Understand the theory and intuition behind Convolutional Neural Networks
- Task 6: Build deep learning model
- Task 7: Compile and train deep learning model
- Task 8: Assess trained model performance
$\color{skyblue}{\textbf{Connect with me:}}$
