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Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. 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.
Features
- Modular, extensible design
- Allows users to plug custom module implementations
- Faster R-CNN, Mask R-CNN, RetinaNet, and DensePose models
- Synchronous Batch Norm and support for new datasets
- Supports object detection with boxes and instance segmentation masks
- Supports semantic segmentation and panoptic segmentation
Project Samples
