How does YOLOv8 perform (or it's used for) segmentation?
I tried looking on google and all I found was how to use it.
YOLOv8 model does not yet have published paper which means that we lack direct insight into their direct research methodology during its creation. However, on the GitHub issues of ultralytics itself, there has a contributor named RangeKing who provided a brief summary of YOLOv8 model structure visually. You can explore it via this link Brief summary of YOLOv8 model structure
Moreover, in the same GitHub issues, Glenn Jocher, founder of Ultralytics, has provided a brief summary of the structure of the YOLOv8-segmentation model. This summary describes the model's architecture and provides insight into how it works and i think that is the one that you are looking for. Here is the brief summary:
The YOLOv8-Seg model is an extension of the YOLOv8 object detection model that also performs semantic segmentation of the input image. The backbone of the YOLOv8-Seg model is a CSPDarknet53 feature extractor, which is followed by a novel C2f module instead of the traditional YOLO neck architecture. The C2f module is followed by two segmentation heads, which learn to predict the semantic segmentation masks for the input image. The model has similar detection heads to YOLOv8, consisting of five detection modules and a prediction layer. The YOLOv8-Seg model has been shown to achieve state-of-the-art results on a variety of object detection and semantic segmentation benchmarks while maintaining high speed and efficiency.
For more detailed information, you can visit that GitHub issues via the link attached above.
Hope this help!