This repository hosts the codebase for a research project that utilizes Squeeze-and-Excitation Networks (SENet) combined with an Attention Mechanism to automate the detection of coffee leaf diseases. This innovative approach leverages deep learning techniques to enhance the accuracy of classifying diseases across coffee plantations by focusing on crucial leaf areas.
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This GitHub repository contains the code for a research project that employs Squeeze-and-Excitation Networks (SENet) and an Attention Mechanism to automatically detect diseases in coffee leaves. The project utilizes deep learning methods to improve disease classification accuracy by concentrating on important areas of the leaves.
izzybug/SENet
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This GitHub repository contains the code for a research project that employs Squeeze-and-Excitation Networks (SENet) and an Attention Mechanism to automatically detect diseases in coffee leaves. The project utilizes deep learning methods to improve disease classification accuracy by concentrating on important areas of the leaves.
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