Summary
This chapter discussed advanced CV tasks, including object detection, instance segmentation, keypoint detection, semantic segmentation, and panoptic segmentation, and when to use them. Detectron2 is a framework that helps implement cutting-edge algorithms for these CV tasks with the advantages of being faster, more accurate, modular, customizable, and built on top of PyTorch. Its architecture has four main parts: input data, backbone, region proposal, and region of interest heads. Each of these components is replaceable with a custom implementation. This chapter also provided the steps to set up a cloud development environment using Google Colab, a local development environment, or to connect Google Colab to a local runtime if needed.
You now understand the leading CV tasks Detectron2 can help develop and have set up a development environment. The next chapter (Chapter 2) will guide you through the steps to build CV applications for all the listed CV tasks using the cutting-edge models provided in the Detectron2 Model Zoo.