In previous chapters, we learned about various object detection algorithms. In this section, we will learn about the Detectron2 platform (https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/) before we implement it using the Google Open Images dataset. Detectron2 is a platform built by the Facebook team. Detectron2 includes high-quality implementations of state-of-the-art object detection algorithms, including DensePose of the Mask R-CNN model family. The original Detectron framework was written in Caffe2, while the Detectron2 framework is written using PyTorch.
Detectron2 supports a range of tasks related to object detection. Like the original Detectron, it supports object detection with boxes and instance segmentation masks, as well as human pose prediction. Beyond that, Detectron2 adds support for semantic segmentation and panoptic segmentation (a task that combines both semantic and instance segmentation). By...
