Multi-Class Image Classification Model for Belgium Traffic Sign Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Belgium Traffic Sign dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset contains over 7,200 images of 62 varieties of traffic signs used in Belgium. The researcher performed experiments on the dataset to create a CNN-based classification system.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 98.58% after 10 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 91.87%.

CONCLUSION: In this iteration, the TensorFlow ResNet50V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Belgium_Traffic_Sign_image_data_62_class_data

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.