Object Detection Using R-CNN, SSD, and R-FCN

In Chapter 7, Object Detection Using YOLO, we learned about YOLO object detection, and then, in the previous two chapters, we learned about action recognition and image in-painting. This chapter marks the beginning of the end-to-end (E2E) object detection framework by developing a solid foundation of data ingestion and training pipeline followed by model development. Here, we will gain a deep insight into the various object detection models, such as R-CNN, single-shot detector (SSD), region-based fully convolutional networks (R-FCNs), and Mask R-CNN, and perform hands-on exercises using Google Cloud and Google Colab notebooks. We will also carry out a detailed exercise on how to train your own custom image to develop an object detection model using a TensorFlow object detection API. We will end the chapter with a deep overview of various...