Multi-Class Image Classification Model for Mango Varieties Grading 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 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 InceptionV3 model’s performance achieved an accuracy score of 94.63% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 61.67%.

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: 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 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 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 ResNet50V2 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 85.00%.

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: 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 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 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 99.79% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 96.88%.

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

One source of potential performance benchmarks: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Mango Varieties 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 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 InceptionV3 model’s performance achieved an accuracy score of 99.31% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 81.88%.

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: 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

One source of potential performance benchmarks: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification/code

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Mango Varieties 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 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 ResNet50V2 model’s performance achieved an accuracy score of 99.10% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 88.13%.

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: 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

One source of potential performance benchmarks: https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification/code

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Concrete Crack Images Using Python and 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 Concrete Crack Images dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to recognize whether there are cracks on concrete surfaces. The dataset contains concrete images with different surface finishes and illumination conditions. The photos were collected from various college campus buildings. The dataset is divided into two classes, negative and positive, and each type has 20,000 images with 227 x 227 pixels.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 99.74% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.86%.

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

Dataset ML Model: Binary-Class classification with numerical features

Dataset Used: Özgenel, Çağlar Fırat (2019), “Concrete Crack Images for Classification”, Mendeley Data, V2, doi: 10.17632/5y9wdsg2zt.2

Dataset Reference: https://data.mendeley.com/datasets/5y9wdsg2zt/2

One source of potential performance benchmarks: https://www.kaggle.com/datasets/arunrk7/surface-crack-detection

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Concrete Crack Images Using Python and 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 Concrete Crack Images dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to recognize whether there are cracks on concrete surfaces. The dataset contains concrete images with different surface finishes and illumination conditions. The photos were collected from various college campus buildings. The dataset is divided into two classes, negative and positive, and each type has 20,000 images with 227 x 227 pixels.

ANALYSIS: The EfficientNetV2S model’s performance achieved an accuracy score of 99.76% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.76%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2S CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Binary-Class classification with numerical features

Dataset Used: Özgenel, Çağlar Fırat (2019), “Concrete Crack Images for Classification”, Mendeley Data, V2, doi: 10.17632/5y9wdsg2zt.2

Dataset Reference: https://data.mendeley.com/datasets/5y9wdsg2zt/2

One source of potential performance benchmarks: https://www.kaggle.com/datasets/arunrk7/surface-crack-detection

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Model for Concrete Crack Images Using Python and TensorFlow Take 3

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. [https://machinelearningmastery.com/]

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 Concrete Crack Images dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to recognize whether there are cracks on concrete surfaces. The dataset contains concrete images with different surface finishes and illumination conditions. The photos were collected from various college campus buildings. The dataset is divided into two classes, negative and positive, and each type has 20,000 images with 227 x 227 pixels.

ANALYSIS: The VGG19 model’s performance achieved an accuracy score of 99.65% after 5 epochs using a separate validation dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 99.83%.

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

Dataset ML Model: Binary-Class classification with numerical features

Dataset Used: Özgenel, Çağlar Fırat (2019), “Concrete Crack Images for Classification”, Mendeley Data, V2, doi: 10.17632/5y9wdsg2zt.2

Dataset Reference: https://data.mendeley.com/datasets/5y9wdsg2zt/2

One source of potential performance benchmarks: https://www.kaggle.com/datasets/arunrk7/surface-crack-detection

The HTML formatted report can be found here on GitHub.