How Synthetic Data Sets Can Improve Computer Vision Models

In recent years, deep learning models have produced a substantial amount of advances in various areas, including computer vision. Computer vision typically usually works by analysing images that have been captured using the physical camera sensor, followed by a human-in-the-loop process that requires annotators to label things of interest. It's important to note that the more sophisticated the annotation is, the more laborious labelling can be. But it provides for a much richer analysis of the image itself. For example, for spotting a tiny detail within an image, a simple bounding box around the object might suffice. But once you start looking to get a robot to grasp something, you might need a segmentation mask to flesh out the fine contours of the object.