Tag: computer vision

Multi-Class Image Classification Model for Grapevine Leaves Image Using TensorFlow Take 5

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 Grapevine Leaves Image Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. Therefore, the species of grapevine leaves are essential factors in price and taste. The research team deployed deep learning-based classification models in this study to identify grapevine leaves. For this study, 500 images of vine leaves make up the dataset.

ANALYSIS: The EfficientNetV2L model’s performance achieved an accuracy score of 90.89% after 30 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 90.00%.

CONCLUSION: In this iteration, the TensorFlow EfficientNetV2L CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Grapevine Leaves Image Dataset

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.measurement.2021.110425

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Grapevine Leaves Image Using TensorFlow Take 4

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 Grapevine Leaves Image Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. Therefore, the species of grapevine leaves are essential factors in price and taste. The research team deployed deep learning-based classification models in this study to identify grapevine leaves. For this study, 500 images of vine leaves make up the dataset.

ANALYSIS: The InceptionV3 model’s performance achieved an accuracy score of 94.89% after 30 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 92.00%.

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: Grapevine Leaves Image Dataset

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.measurement.2021.110425

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Grapevine Leaves Image Using TensorFlow Take 3

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 Grapevine Leaves Image Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. Therefore, the species of grapevine leaves are essential factors in price and taste. The research team deployed deep learning-based classification models in this study to identify grapevine leaves. For this study, 500 images of vine leaves make up the dataset.

ANALYSIS: The MobileNetV2 model’s performance achieved an accuracy score of 94.89% after 30 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 74.00%.

CONCLUSION: In this iteration, the TensorFlow MobileNetV2 CNN model appeared suitable for modeling this dataset.

Dataset ML Model: Multi-Class classification with numerical features

Dataset Used: Grapevine Leaves Image Dataset

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.measurement.2021.110425

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Grapevine Leaves Image Using TensorFlow Take 2

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 Grapevine Leaves Image Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. Therefore, the species of grapevine leaves are essential factors in price and taste. The research team deployed deep learning-based classification models in this study to identify grapevine leaves. For this study, 500 images of vine leaves make up the dataset.

ANALYSIS: The ResNet50V2 model’s performance achieved an accuracy score of 82.67% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 84.00%.

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: Grapevine Leaves Image Dataset

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.measurement.2021.110425

The HTML formatted report can be found here on GitHub.

Multi-Class Image Classification Model for Grapevine Leaves Image Using TensorFlow Take 1

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 Grapevine Leaves Image Dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. Therefore, the species of grapevine leaves are essential factors in price and taste. The research team deployed deep learning-based classification models in this study to identify grapevine leaves. For this study, 500 images of vine leaves make up the dataset.

ANALYSIS: The Xception model’s performance achieved an accuracy score of 89.78% after 20 epochs using the training dataset. When we applied the model to the validation dataset, the model achieved an accuracy score of 86.00%.

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: Grapevine Leaves Image Dataset

Dataset Reference: https://www.muratkoklu.com/datasets/

One source of potential performance benchmarks: https://doi.org/10.1016/j.measurement.2021.110425

The HTML formatted report can be found here on GitHub.