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 EfficientNetV2L model’s performance achieved an accuracy score of 98.68% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 97.92%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2L CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Normal vs. Military Vehicles

Dataset Reference: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles

One source of potential performance benchmarks: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles/code

The HTML formatted report can be found here on GitHub.

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 VGG19 model’s performance achieved an accuracy score of 97.65% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 97.27%.

CONCLUSION: In this iteration, the TensorFlow VGG19 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Normal vs. Military Vehicles

Dataset Reference: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles

One source of potential performance benchmarks: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles/code

The HTML formatted report can be found here on GitHub. [https://github.com/daines-analytics/deep-learning-projects/tree/master/py_tensorflow_binaryclass_amanrajbose_normal_military_vehicles]

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 MobileNetV2 model’s performance achieved an accuracy score of 98.31% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 98.38%.

CONCLUSION: In this iteration, the TensorFlow MobileNetV2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Normal vs. Military Vehicles

Dataset Reference: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles

One source of potential performance benchmarks: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles/code

The HTML formatted report can be found here on GitHub.

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 ResNet152V2 model’s performance achieved an accuracy score of 97.54% after five epochs using the validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 97.27%.

CONCLUSION: In this iteration, the TensorFlow ResNet152V2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Normal vs. Military Vehicles

Dataset Reference: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles

One source of potential performance benchmarks: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles/code

The HTML formatted report can be found here on GitHub.

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: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles

One source of potential performance benchmarks: https://www.kaggle.com/datasets/amanrajbose/normal-vs-military-vehicles/code

The HTML formatted report can be found here on GitHub.