Peachypie98/CBAM
CBAM: Convolutional Block Attention Module for CIFAR100 on VGG19
This project helps machine learning engineers improve how their convolutional neural networks (CNNs) process images. By integrating this module, your CNN can better focus on the most relevant parts of an image (what to attend to) and the most important regions within it (where to attend to). The result is a more accurate image classification or object detection system for applications like identifying objects in photos or analyzing medical scans.
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Use this if you are a machine learning engineer working with convolutional neural networks and want to enhance their feature representation capabilities for improved performance without significant architectural changes.
Not ideal if you are not working with deep learning models, specifically convolutional neural networks, or if you are looking for a pre-trained, ready-to-use image classification model rather than a modular component.
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May 15, 2025
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