Deep Learning with PyTorch : GradCAM

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In this Free Guided Project, you will:
2 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

Gradient-weighted Class Activation Mapping (Grad-CAM), uses the class-specific gradient information flowing into the final convolutional layer of a CNN to produce a coarse localization map of the important regions in the image. In this 2-hour long project-based course, you will implement GradCAM on simple classification dataset. You will write a custom dataset class for Image-Classification dataset. Thereafter, you will create custom CNN architecture. Moreover, you are going to create train function and evaluator function which will be helpful to write the training loop. After, saving the best model, you will write GradCAM function which return the heatmap of localization map of a given class. Lastly, you plot the heatmap which the given input image.

Requirements

Skills you will develop

  • Deep Learning

  • GradCAM

  • Convolutional Neural Network

  • pytorch

  • Computer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

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In a split-screen video, your instructor guides you step-by-step

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