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返回到 Deep Learning with PyTorch : GradCAM

学生对 Coursera 提供的 Deep Learning with PyTorch : GradCAM 的评价和反馈

4.6
21 个评分

课程概述

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....

热门审阅

SS

Feb 20, 2023

Great material, easy to follow and to some extent helps build intuition.

SF

Aug 31, 2025

Very good project. Helps you implement gradcam from grounds up.

筛选依据:

1 - Deep Learning with PyTorch : GradCAM 的 4 个评论(共 4 个)

创建者 Sayantan S

Feb 21, 2023

Great material, easy to follow and to some extent helps build intuition.

创建者 Shaheer F

Sep 1, 2025

Very good project. Helps you implement gradcam from grounds up.

创建者 Jafeth G

Aug 19, 2025

Genial

创建者 Harith Y

Jan 11, 2025

Please explain more in detail and also cover some important prerequisities.