mohamedamine99/Visualizing-what-convnets-learn
This Github repository explains the impact of different activation functions on CNN's performance and provides visualizations of activations, convnet filters, and heatmaps of class activation for easier understanding of how CNN works.
This project helps machine learning practitioners understand how Convolutional Neural Networks (CNNs) process and classify images. It takes an image as input and generates visualizations of the network's internal activations, filters, and heatmaps, showing which parts of an image are most important for classification. This is ideal for machine learning engineers, data scientists, and students working with computer vision models.
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Use this if you need to demystify the 'black box' nature of CNNs and gain intuitive insights into their decision-making process for image classification.
Not ideal if you are looking for a tool to build or train new CNN models, or if your focus is on model deployment rather than interpretability.
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Jun 15, 2023
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