#instance segmentation

SAHI provides an efficient vision library for large-scale object detection and segmentation. It tackles the challenges of small object detection and large image inference, enhancing accuracy in practical applications. Supporting frameworks like YOLOv5, MMDetection, and Detectron2, it seamlessly integrates into current workflows. Features include sliced and standard prediction, COCO dataset functionalities, error analysis, and interactive visualization. Widely adopted with over 200 citations and competitive successes, SAHI advances computer vision technology.

Learn about the QueryInst method, a query-based instance segmentation approach that offers enhanced accuracy and speed through parallel supervision. Understand its effectiveness in object detection, instance, and video segmentation. Access seamless integration with mmdetection and review its performance on the COCO benchmark. Follow guidance on employing this method using available configurations and checkpoints, and explore its application in expansive instance-level recognition tasks.

The SOLO project introduces compelling instance segmentation algorithms that emphasize accuracy and efficiency. This includes SOLO and its advanced version, SOLOv2, both achieving significant improvements in accuracy on COCO test-dev datasets. The algorithms offer direct instance segmentation, producing fine, detailed masks without anchor dependency, and maintain high-speed operations, even in lightweight configurations. Originating in 2020, the project has undergone updates that accelerate training by 1.7 times and support integrations with frameworks such as detectron2, making it an attractive option for researchers and developers interested in advanced segmentation techniques.