# Panoptic Segmentation using Detectron2

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](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](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/](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