Multi-Class Image Classification Model for Belgium Traffic Sign 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 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 EfficientNetV2S model’s performance achieved an accuracy score of 99.41% after 10 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 94.05%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2S 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.

Multi-Class Image Classification Model for Belgium Traffic Sign 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 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 VGG19 model’s performance achieved an accuracy score of 98.93% after 10 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 93.85%.

CONCLUSION: In this iteration, the TensorFlow VGG19 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.

Multi-Class Image Classification Model for Belgium Traffic Sign 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 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.

Multi-Class Image Classification Model for Belgium Traffic Sign 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 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 InceptionV3 model’s performance achieved an accuracy score of 98.84% after 10 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 93.73%.

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: 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.

Multi-Class Image Classification Model for Belgium Traffic Sign 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 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 ResNet50V2 model’s performance achieved an accuracy score of 98.58% after 10 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 91.87%.

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: 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.

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

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 94.81% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 85.00%.

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: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

The HTML formatted report can be found here on GitHub.

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

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The VGG19 model’s performance achieved an accuracy score of 97.59% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 83.33%.

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

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

The HTML formatted report can be found here on GitHub.

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

INTRODUCTION: This dataset contains 1,600 images of eight varieties of Pakistani mangoes. The research team performed experiments on the dataset to create an automated classification and grading system. The system classifies the harvested mangoes for farmers to deliver high-quality mangoes for export with high accuracy.

ANALYSIS: The DenseNet201 model’s performance achieved an accuracy score of 94.44% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 80.00%.

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: Mango Varieties Classification and Grading, Rizwan Iqbal, Hafiz Muhammad; Hakim, Ayesha (2021), “Mango Variety and Grading Dataset,” Mendeley Data, V1, DOI: 10.17632/5mc3s86982.1

Dataset Reference: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

The HTML formatted report can be found here on GitHub.