Multi-Class Image Classification Deep Learning Model for Textile Defect Detection 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 Textile Defect Detection dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset from Kaggle contains 96,000 patches of the textile image with different quality problems. The goal of the exercise is to detect the quality issue for a patch of textile during production. The greyscale photos are part of the public dataset made available by the MVTec Company and referred by the research paper from Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger. MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.

From iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a separate test dataset.

From iteration Take2, we constructed a CNN model using the DenseNet201 architecture and tested the model’s performance using a separate test dataset.

In this Take3 iteration, we will construct a CNN model using the ResNet50V2 architecture and test the model’s performance using a separate test dataset.

ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 97.07% on the validation dataset after 15 epochs. Furthermore, the final model’s performance achieved an accuracy score of 68.03% on the test dataset after 15 epochs.

From iteration Take2, the DenseNet201 model’s performance achieved an accuracy score of 98.93% on the validation dataset after 15 epochs. Furthermore, the final model’s performance achieved an accuracy score of 53.69% on the test dataset after 15 epochs.

In this Take3 iteration, the ResNet50V2 model’s performance achieved an accuracy score of 94.88% on the validation dataset after 15 epochs. Furthermore, the final model’s performance achieved an accuracy score of 59.12% on the test dataset after 15 epochs.

CONCLUSION: In this iteration, the ResNet50V2 CNN model did not appear suitable for modeling this dataset due to a high-variance problem. We should consider experimenting with more or different data for further modeling.

Dataset Used: Textile Defect Detection

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://www.kaggle.com/belkhirnacim/textiledefectdetection

A potential source of performance benchmarks: https://www.kaggle.com/belkhirnacim/textiledefectdetection

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for Textile Defect Detection 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 Textile Defect Detection dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset from Kaggle contains 96,000 patches of the textile image with different quality problems. The goal of the exercise is to detect the quality issue for a patch of textile during production. The greyscale photos are part of the public dataset made available by the MVTec Company and referred by the research paper from Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger. MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.

From iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a separate test dataset.

In this Take2 iteration, we will construct a CNN model using the DenseNet201 architecture and test the model’s performance using a separate test dataset.

ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 97.07% on the validation dataset after 15 epochs. Furthermore, the final model’s performance achieved an accuracy score of 68.03% on the test dataset after 15 epochs.

In this Take2 iteration, the DenseNet201 model’s performance achieved an accuracy score of 98.93% on the validation dataset after 15 epochs. Furthermore, the final model’s performance achieved an accuracy score of 53.69% on the test dataset after 15 epochs.

CONCLUSION: In this iteration, the DenseNet201 CNN model did not appear suitable for modeling this dataset due to a high-variance problem. We should consider experimenting with more or different data for further modeling.

Dataset Used: Textile Defect Detection

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://www.kaggle.com/belkhirnacim/textiledefectdetection

A potential source of performance benchmarks: https://www.kaggle.com/belkhirnacim/textiledefectdetection

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for Textile Defect Detection 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 Textile Defect Detection dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset from Kaggle contains 96,000 patches of the textile image with different quality problems. The goal of the exercise is to detect the quality issue for a patch of textile during production. The greyscale photos are part of the public dataset made available by the MVTec Company and referred by the research paper from Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger. MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.

In this Take1 iteration, we will construct a CNN model using a simple three-block VGG architecture and test the model’s performance using a separate test dataset.

ANALYSIS: In this Take1 iteration, the baseline model’s performance achieved an accuracy score of 97.07% on the validation dataset after 15 epochs. Furthermore, the final model’s performance achieved an accuracy score of 68.03% on the test dataset after 15 epochs.

CONCLUSION: In this iteration, the simple three-block VGG CNN model did not appear suitable for modeling this dataset due to a high-variance problem. We should consider experimenting with more or different data for further modeling.

Dataset Used: Textile Defect Detection

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://www.kaggle.com/belkhirnacim/textiledefectdetection

A potential source of performance benchmarks: https://www.kaggle.com/belkhirnacim/textiledefectdetection

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Deep Learning Model for Malaria Parasite Detection 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 Malaria Parasite Detection dataset is a binary-class classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Biomedical researchers have developed a mobile application that runs on a standard Android smartphone attached to a conventional light microscope for detecting malaria disease. The smartphone’s built-in camera acquired thin blood smear images of slides for each microscopic field of view. An expert manually annotated the slides afterward. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

From iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a held-out validation dataset.

From iteration Take2, we constructed a CNN model using the InceptionV3 architecture and tested the model’s performance using a held-out validation dataset.

From iteration Take3, we constructed a CNN model using the ResNet50V2 architecture and tested the model’s performance using a held-out validation dataset.

In this Take4 iteration, we will construct a CNN model using the DenseNet201 architecture and test the model’s performance using a held-out validation dataset.

ANALYSIS: From iteration Take1, the model’s performance achieved an average accuracy score of 94.08% on the validation dataset after 20 epochs.

From iteration Take2, the model’s performance achieved an average accuracy score of 95.12% on the validation dataset after 20 epochs.

From iteration Take3, the model’s performance achieved an average accuracy score of 95.19% on the validation dataset after 20 epochs.

In this Take4 iteration, the model’s performance achieved an average accuracy score of 95.41% on the validation dataset after 20 epochs.

CONCLUSION: In this iteration, the DenseNet201 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Malaria Parasite Detection

Dataset ML Model: Binary-class image classification with numerical attributes

Dataset Reference: https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html

A potential source of performance benchmark: https://doi.org/10.7717/peerj.4568 or https://doi.org/10.7717/peerj.6977

One potential source of performance benchmarks: https://www.kaggle.com/c/cassava-leaf-disease-classification/leaderboard

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Deep Learning Model for Malaria Parasite Detection 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 Malaria Parasite Detection dataset is a binary-class classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Biomedical researchers have developed a mobile application that runs on a standard Android smartphone attached to a conventional light microscope for detecting malaria disease. The smartphone’s built-in camera acquired thin blood smear images of slides for each microscopic field of view. An expert manually annotated the slides afterward. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

From iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a held-out validation dataset.

From iteration Take2, we constructed a CNN model using the InceptionV3 architecture and tested the model’s performance using a held-out validation dataset.

In this Take3 iteration, we will construct a CNN model using the ResNet50V2 architecture and test the model’s performance using a held-out validation dataset.

ANALYSIS: From iteration Take1, the model’s performance achieved an average accuracy score of 94.08% on the validation dataset after 20 epochs.

From iteration Take2, the model’s performance achieved an average accuracy score of 95.12% on the validation dataset after 20 epochs.

In this Take3 iteration, the model’s performance achieved an average accuracy score of 95.19% on the validation dataset after 20 epochs.

CONCLUSION: In this iteration, the ResNet50V2 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Malaria Parasite Detection

Dataset ML Model: Binary-class image classification with numerical attributes

Dataset Reference: https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html

A potential source of performance benchmark: https://doi.org/10.7717/peerj.4568 or https://doi.org/10.7717/peerj.6977

One potential source of performance benchmarks: https://www.kaggle.com/c/cassava-leaf-disease-classification/leaderboard

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Deep Learning Model for Malaria Parasite Detection 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 Malaria Parasite Detection dataset is a binary-class classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Biomedical researchers have developed a mobile application that runs on a standard Android smartphone attached to a conventional light microscope for detecting malaria disease. The smartphone’s built-in camera acquired thin blood smear images of slides for each microscopic field of view. An expert manually annotated the slides afterward. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

From iteration Take1, we constructed a CNN model using a simple three-block VGG architecture and tested the model’s performance using a held-out validation dataset.

In this Take2 iteration, we will construct a CNN model using the InceptionV3 architecture and test the model’s performance using a held-out validation dataset.

ANALYSIS: From iteration Take1, the model’s performance achieved an average accuracy score of 94.08% on the validation dataset after 20 epochs.

In this Take2 iteration, the model’s performance achieved an average accuracy score of 95.12% on the validation dataset after 20 epochs.

CONCLUSION: In this iteration, the InceptionV3 CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Malaria Parasite Detection

Dataset ML Model: Binary-class image classification with numerical attributes

Dataset Reference: https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html

A potential source of performance benchmark: https://doi.org/10.7717/peerj.4568 or https://doi.org/10.7717/peerj.6977

One potential source of performance benchmarks: https://www.kaggle.com/c/cassava-leaf-disease-classification/leaderboard

The HTML formatted report can be found here on GitHub.

Binary-Class Image Classification Deep Learning Model for Malaria Parasite Detection 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 Malaria Parasite Detection dataset is a binary-class classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Biomedical researchers have developed a mobile application that runs on a standard Android smartphone attached to a conventional light microscope for detecting malaria disease. The smartphone’s built-in camera acquired thin blood smear images of slides for each microscopic field of view. An expert manually annotated the slides afterward. The dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells.

In this Take1 iteration, we will construct a CNN model using a simple three-block VGG architecture and test the model’s performance using a held-out validation dataset.

ANALYSIS: In this Take1 iteration, the model’s performance achieved an average accuracy score of 94.08% on the validation dataset after 20 epochs.

CONCLUSION: In this iteration, the simple three-block VGG CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Malaria Parasite Detection

Dataset ML Model: Binary-class image classification with numerical attributes

Dataset Reference: https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html

A potential source of performance benchmark: https://doi.org/10.7717/peerj.4568 or https://doi.org/10.7717/peerj.6977

One potential source of performance benchmarks: https://www.kaggle.com/c/cassava-leaf-disease-classification/leaderboard

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Deep Learning Model for Cassava Leaf Disease 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 Cassava Leaf Disease dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: As the second-largest provider of carbohydrates in Africa, cassava is an essential food security crop grown by smallholder farmers because it can withstand harsh conditions. Existing disease detection methods require farmers to solicit government-funded agricultural experts’ help to visually inspect and diagnose the plants. This method suffers from being labor-intensive, low-supply, and costly.

The research team compiled a dataset of 21,367 labeled images collected during a regular survey in Uganda to address the problem. Most pictures were crowdsourced from farmers taking photos of their gardens and annotated by experts at the National Crops Resources Research Institute (NaCRRI) in collaboration with the AI lab at Makerere University, Kampala. Our task is to classify each cassava image into four disease categories or a fifth category indicating a healthy leaf.

From iteration Take1, we constructed a CNN model using the InceptionV3 architecture and tested the model’s performance using cross-validation. Also, we submitted our model to Kaggle and tested the model’s performance using Kaggle’s test images.

From iteration Take2, we constructed a CNN model using the ResNet50V2 architecture and tested the model’s performance using cross-validation. Also, we submitted our model to Kaggle and tested the model’s performance using Kaggle’s test images.

In this Take3 iteration, we will construct a CNN model using the DenseNet201 architecture and test the model’s performance using cross-validation. Also, we will submit our model to Kaggle and test the model’s performance using Kaggle’s test images.

ANALYSIS: From iteration Take1, the model’s performance achieved an average accuracy score of 67.17% on the validation dataset after 30 epochs. Furthermore, the final model processed Kaggle’s test dataset with an accuracy measurement of 61.25%.

From iteration Take2, the model’s performance achieved an average accuracy score of 61.86% on the validation dataset after 30 epochs. Furthermore, the final model processed Kaggle’s test dataset with an accuracy measurement of 61.28%.

In this Take3 iteration, the model’s performance achieved an average accuracy score of 69.50% on the validation dataset after 30 epochs. Furthermore, the final model processed Kaggle’s test dataset with an accuracy measurement of 61.30%.

CONCLUSION: In this iteration, the DenseNet201 TensorFlow CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: Cassava Leaf Disease Classification

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference: https://www.kaggle.com/c/cassava-leaf-disease-classification/

One potential source of performance benchmarks: https://www.kaggle.com/c/cassava-leaf-disease-classification/leaderboard

The HTML formatted report can be found here on GitHub.