YOLACTs

YOLACTs, which stands for "You Only Look At CoefficienTs," is a real-time instance segmentation model. It is an extension of the YOLO (You Only Look Once) object detection framework. The core idea behind YOLACTs is to perform instance segmentation efficiently by generating a set of prototype masks and then predicting coefficients for each detected object. These coefficients are then used to linearly combine the prototype masks, effectively creating a segmentation mask for each individual object.

This approach allows YOLACTs to achieve high speed while maintaining competitive accuracy. Unlike traditional methods that often involve complex post-processing steps, YOLACTs directly predicts segmentation masks in a single, unified network. This makes it suitable for applications requiring real-time performance, such as autonomous driving or robotic vision. The model is designed to be end-to-end trainable, simplifying the training process. YOLACTs has been a significant contribution to the field of real-time computer vision, particularly in the domain of object detection and segmentation.