facebookresearch/detectron2

What it solves

Detectron2 provides a high-performance library for state-of-the-art object detection and segmentation algorithms, serving as a modular successor to Detectron and maskrcnn-benchmark.

How it works

It functions as a flexible library that researchers and developers can build upon to create computer vision projects. It includes a wide array of pre-trained models (Model Zoo) and supports exporting models to TorchScript or Caffe2 formats for production deployment.

Who it’s for

It is designed for computer vision researchers and developers building production applications that require advanced image analysis capabilities.

Highlights

• Supports advanced capabilities like panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, and ViTDet.
• Offers faster training speeds compared to its predecessors.
• Provides a comprehensive Model Zoo with baseline results and downloadable trained models.
• Modular design allows it to be used as a foundation for other research projects.