Yolo det vis

[hlmhlr]

Hi,

To visualize the visualize class activation maps (CAM) of the deep neural network, I have made some modifications in the code of @pourmand1376 explainer.py from the link to make it more robust regarding CAM output visualization by taking the idea from this link..

Mainly the following changes are made:

• Concerning object detection, removing heatmap data outside of the bounding boxes and scaling the heatmaps within each box.
• Drawing the bounding boxes for a specific class(es) if mentioned via argument otherwise for all classes.
• Showcasing or hiding the class labels. For example, if there is an aerial image with a large number of objects, then visualizing the labels would limit the analysis of surrounding objects.
• Visualizing the classwise activation maps with/without drawing boxes.

File 'explainer.py' (on this link ) will save four images (CAM image, heatmap, CAM with/without boxes drawn, concatenated image to visualize original, CAM and CAM with boxes images at once) for every single input image. However, the run function will return three images (cam image, cam image with boxes, and concatenated image) for the last input image just to have better visualization and analysis.

The updated colab notebook is available on this link where the results can be generated and verified.

[github-actions]

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[github-actions]

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