Detectron2: A PyTorch-based modular object detection library
“Since its release in 2018, the Detectron object detection platform has become one of Facebook AI Research (FAIR)’s most widely adopted open source projects. To build on and advance this project, we are now sharing the second generation of the library, with important enhancements for both research and production use. It is available here.
Detectron2 is a ground-up rewrite of Detectron that started with maskrcnn-benchmark. The platform is now implemented in PyTorch. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection algorithms, including DensePose, panoptic feature pyramid networks, and numerous variants of the pioneering Mask R-CNN model family also developed by FAIR. Its extensible design makes it easy to implement cutting-edge research projects without having to fork the entire codebase.
We built Detectron2 to meet the research needs of Facebook AI and to provide the foundation for object detection in production use cases at Facebook. We are now using Detectron2 to rapidly design and train the next-generation pose detection models that power Smart Camera, the AI camera system in Facebook’s Portal video-calling devices. By relying on Detectron2 as the unified library for object detection across research and production use cases, we are able to rapidly move research ideas into production models that are deployed at scale…”