Applying active ML to an object detection project
In this section, we will guide you through the implementation of active ML techniques for an object detection project. An object detection project refers to developing a computer vision model to detect and localize objects within images or videos. The dataset is a collection of images (video frames) containing examples of the objects you want to detect, among other things. The dataset needs to have labels in the form of bounding boxes around the objects. Popular datasets for this purpose include COCO (https://cocodataset.org/), PASCAL VOC (http://host.robots.ox.ac.uk/pascal/VOC/), and OpenImages (https://storage.googleapis.com/openimages/web/index.html). The model architecture uses a neural network designed for object detection such as Faster R-CNN, YOLO, and so on. This type of architecture can automatically identify and localize real-world objects within visual data. The end result is a model that can detect and draw boxes around...
