Panoptic Segmentation using Detectron2
I am a computer vision engineer, focused on image classification based on CNN and transfer learning and OpenCV
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For anyone studying Panoptic Segmentation using Detectron2, this tutorial walks through how panoptic segmentation combines instance segmentation (separating individual objects) and semantic segmentation (labeling background regions), so you get a complete pixel-level understanding of a scene.
It uses Detectron2’s pretrained COCO panoptic model from the Model Zoo, then shows the full inference workflow in Python: reading an image with OpenCV, resizing it for faster processing, loading the panoptic configuration and weights, running prediction, and visualizing the merged “things and stuff” output.
Video explanation: https://youtu.be/MuzNooUNZSY
Medium version for readers who prefer Medium : https://medium.com/image-segmentation-tutorials/detectron2-panoptic-segmentation-made-easy-for-beginners-9f56319bb6cc
Written explanation with code: https://eranfeit.net/detectron2-panoptic-segmentation-made-easy-for-beginners/
This content is shared for educational purposes only, and constructive feedback or discussion is welcome.
Eran Feit
