Introducing Semantic Segmentation and Object Detection using COCO format.
As you already know, it started with just a research paper, and a tiny open source community about semantic segmentation and object detection. On the recent release of the COCO growing data set , there are familiar faces, researchers and companies as collaborators Cornell’s computer vision guru: Serge Belongie, Mappilary and others.
COCO allows to annotate images with polygons and record the pixels for semantic segmentation and masks. It also picks the alternative bounding boxes for object detection.
What worked best for us using COCO format with our client projects:
Scene Image segmentation for robotics (industrial context) and street view cameras for autonomous driving or contextual cases (traffic management).
Complex ML models where some of the objects are readily available using off the shelf solutions. The others require precision, and it is impossible to say whether a full segmentation or bounding boxes would be enough.
Building instance segmentation datasets.