Summary

In this chapter, we learned about the various practical aspects of dealing with object localization and segmentation. Specifically, we learned about how the Detectron2 platform is leveraged to perform image segmentation and detection, and keypoint detection. In addition, we also learned about some of the intricacies involved in working with large datasets when we were working on fetching images from the Open Images dataset. Next, we worked on leveraging the VGG and U-Net architectures for crowd counting and image colorization, respectively. Then, we understood the theory and implementation steps behind 3D object detection using point cloud images. Finally, we understood ways of performing classification exercises on a sequence of frames (video). As you can see from all these examples, the underlying basics are the same as those described in the previous chapters, with modifications only in the input/output of the networks to accommodate the task at hand.

In the next chapter...