Explain an explainable AI algorithm GradCAM, covered the intuition, mathematics and coding of this technique, also GradCAM++ and ScoreCAM

Notebook: https://github.com/JimEverest/CAM/blob/mai...

In the context of classification, we generate a Class Activation Map (CAM) for a specific class. For regression tasks, we need to define a similar concept, although this will be highly task-dependent. For instance, if we are concerned with the regression of bounding box coordinates, we may assign a score based on the Intersection over Union (IoU) metric. In the case of classification scores, we can directly assign the score. Alternatively, we could consider a combination of both the class and a property of the bounding box coordinates, such as an aggregate score comprising the IoU and the classification score.

The underlying principle here is that we need to identify a target scalar (score) so that we can emphasize the regions of the image that contribute most to maximizing that score. By doing so, we can examine the correspondence between the model output and the original detections, ensuring logical coherence and rigor in our analysis.