Month: September 2022

Multi-Class Image Classification Model for Reptiles and Amphibians Image Using TensorFlow Take 7

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 Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The NASNetMobile model’s performance achieved an accuracy score of 81.65% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 80.67%.

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: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Reptiles and Amphibians Image Using TensorFlow Take 6

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 Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 91.51% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 86.17%.

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: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Reptiles and Amphibians Image 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 Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 87.11% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 78.67%.

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: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Reptiles and Amphibians Image 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 Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 86.55% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 73.83%.

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: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Reptiles and Amphibians Image 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 Reptiles and Amphibians Image dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: Reptiles and amphibians are crucial components of the ecosystems in which they reside. They are also useful indicators of the condition of the environment. The presence of a variety of amphibians and reptiles in an area suggests that it is stable and capable of supporting plant and animal life.

The dataset contains ten different classes of reptiles and amphibians images. The research team have resized the images to either (300px, n) or (n,300px) where n is a pixel size less than 300px.

ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 91.15% after ten epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 83.67%.

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: Reptiles and Amphibians Image Dataset

Dataset Reference: https://www.kaggle.com/datasets/vencerlanz09/reptiles-and-amphibians-image-dataset

One source of potential performance benchmarks: https://www.kaggle.com/code/vencerlanz09/reptiles-and-amphibians-classification-using-cnn/notebook

The HTML formatted report can be found here on GitHub.