Binary-Class Image Classification Model for Car vs. Bike Classification Using TensorFlow Take 3

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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 98.35% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.33%.

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

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification Using TensorFlow Take 2

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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 98.47% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 95.00%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary classification with numerical features

Dataset Used: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Car vs. Bike Classification 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 Car vs. Bike Classification Dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The dataset contains 2,000 images of cars and bikes. The research team collected these images to investigate the machine learning model’s ability to understand and distinguish the basic structure of cars and bikes. The research team also made sure that all types of bikes and cars were included for a high degree of variety of cars and bikes.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.56% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 98.33%.

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: Car vs. Bike Classification Dataset

Dataset Reference: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for American Sign Language Alphabet Using TensorFlow Take 5

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 American Sign Language Alphabet Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 99.13% after three epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 89.11%.

CONCLUSION: In this iteration, the TensorFlow InceptionV3 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: American Sign Language Alphabet Dataset

Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for American Sign Language Alphabet Using TensorFlow Take 4

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 American Sign Language Alphabet Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.

ANALYSIS: The NASNetMobile model’s performance achieved an accuracy score of 99.26% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 86.92%.

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

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: American Sign Language Alphabet Dataset

Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for American Sign Language Alphabet Using TensorFlow Take 3

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 American Sign Language Alphabet Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 98.45% after five epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 84.54%.

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: American Sign Language Alphabet Dataset

Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for American Sign Language Alphabet Using TensorFlow Take 2

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 American Sign Language Alphabet Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.

ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 99.01% after three epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 88.47%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: American Sign Language Alphabet Dataset

Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for American Sign Language Alphabet 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 American Sign Language Alphabet Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset contains over 22,000 images of alphabets from American Sign Language, separated into 29 folders that represent the various classes. The research team collected these images to investigate the possibilities of reducing the communication gap between sign-language users and non-Sign language users.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 99.23% after three epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 89.94%.

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

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: American Sign Language Alphabet Dataset

Dataset Reference: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset

One source of potential performance benchmarks: https://www.kaggle.com/datasets/debashishsau/aslamerican-sign-language-aplhabet-dataset/code

The HTML formatted report can be found here on GitHub.