Advanced Computer Vision Tutorial : Understanding VGG16 with Gradcam

In this advanced computer vision tutorial, we explore how to use the popular VGG16 model for image recognition, and how to maximize its performance using the Gradcam technique. With Gradcam, we can visualize the most important areas of an input image that contribute to the VGG16 model's decision-making process, providing a deeper understanding of how the model works.

In this tutorial, we will take you through the process of using the VGG16 model for image recognition, and demonstrate how to use the Gradcam library to visualize the important areas of an image that help the model recognize the relevant object or class. We'll show you how to use the technique on a new test image and mark the specific areas with colors.

This tutorial is designed for those with a good understanding of deep learning and computer vision techniques, and who are looking to take their knowledge to the next level. By the end of this video, you'll have a clear understanding of how to apply the Gradcam technique to VGG16 model and improve the accuracy of your image recognition systems.

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Here is the code for this video: https://github.com/feitgemel/TensorFlowProjects/tree/master/CNN-Visualization

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