Binary-Class Image Classification Model for Military vs. Non-Military Vehicles Using TensorFlow Take 1

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 Normal vs. Military Vehicles dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains over 17,000 images of military and non-military vehicles. The researcher collected these images to investigate the machine learning model’s ability to understand and distinguish the basic features of military and civilian vehicles.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.34% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 98.35%.

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

Dataset ML Model: Binary classification with numerical features

Dataset Used: Normal vs. Military Vehicles

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.