Implementing GradCAM on UNet with PyTorch for Multi-Class Segmentation

In this video, we’ll show you how to apply Grad-CAM (Gradient-weighted Class Activation Mapping) to the U-Net architecture for multi-class semantic segmentation using PyTorch. You’ll learn how to visualize which parts of the image influence the model's decisions the most — a powerful tool for debugging and interpreting deep learning models.

⏱️ Timestamps:
00:00 - Introduction
00:15 - What is GradCAM?
01:00 - Previous Multiclass Segmentation Code
01:40 - GradCAM Implementation
16:00 - Executing the GradCAM & Visualization
20:57 - Final Thoughts & Wrap-up

🔍 What You’ll Learn:
✅ How U-Net works for image segmentation
✅ How to implement Grad-CAM for segmentation models
✅ How to select the right layers for visualization
✅ Visualizing feature maps and model attention

🔗 GitHub Repo: https://github.com/nikhilroxtomar/Multiclass-Segmentation-in-PyTorch

📸 Dataset: https://figshare.com/articles/dataset/Multiclass_Weeds_Dataset_for_Image_Segmentation/22643434?file=40195219

Multiclass Image Segmentation in PyTorch: https://youtu.be/W8lX3Ekao4g

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Видео Implementing GradCAM on UNet with PyTorch for Multi-Class Segmentation канала Idiot Developer