In this chapter, we started with learning about creating a training dataset for the process of object localization and detection. Next, we learned about SelectiveSearch, a region proposal technique that recommends regions based on the similarity of pixels in proximity. We next learned about calculating the IoU metric to understand the goodness of the predicted bounding box around the objects present in the image. We next learned about performing non-max suppression to fetch one bounding box per object within an image before learning about building R-CNN and Fast R-CNN models from scratch. In addition, we learned about the reason why R-CNN is slow and how Fast R-CNN leverages RoI pooling and fetches region proposals from feature maps to make inference faster. Finally, we understood that having region proposals coming from a separate model is resulting in the higher time taken to predict on new images.
In the next chapter, we will learn about some of the modern object detection...
