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 DenseNet201 model’s performance achieved an accuracy score of 99.48% after 10 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.08%.
CONCLUSION: In this iteration, the TensorFlow DenseNet201 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: https://www.kaggle.com/datasets/abhi8923shriv/belgium-ts
One source of potential performance benchmarks: https://www.kaggle.com/datasets/abhi8923shriv/belgium-ts/code
The HTML formatted report can be found here on GitHub.