Add GradCAM integration - Make YOLOv5 Interpretable

[pourmand1376]

Why this PR?

This PR will adapt GradCAM library to YOLOv5. This is required since black box models are not always acceptable. We need to know why a certain prediction was made. This is completely different from feature visualization which is already implemented. This explains the model results on a per image basis. For example, we want to know why the model has detected this Person. What pixels are mostly responsible for this prediction? This will result in a heatmap like this.

EigenCAM layer -2:

EigenCAM layer -3:

Current State

Currently, I've implemented EigenCAM and it works perfectly. Still, I have to write documentation to understand how it works.

Related Issues and Links

This is a long-requested feature. YOLOv5 Related issues:

• #8717
• #5863
• #4575
• #2065

Related Issues in other repositories:

• https://github.com/jacobgil/pytorch-grad-cam/issues/364
• https://github.com/jacobgil/pytorch-grad-cam/issues/359
• https://github.com/jacobgil/pytorch-grad-cam/issues/242

Useful Links:

• https://github.com/pooya-mohammadi/yolov5-gradcam: This one is actually fine but it is too old. Also, It doesn't add this functionality to YOLO in a way that it works with later versions. It implements YOLO from scratch.
• Tutorial: Class Activation Maps for Object Detection with Faster RCNN — Advanced AI explainability with pytorch-gradcam
• EigenCAM for YOLO5 — Advanced AI explainability with pytorch-gradcam