Category: Object Detection

This is the last post in my mini-series on object detection with synthetic data. Over the first four posts, we introduced the problem, discussed some classical synthetic datasets for object detection, talked about some early works that have still relevant conclusions and continued with a case study on retail and food object detection. Today we consider two papers from 2019 that still represent the state of the art in object detection with synthetic data and are often used as generic references to the main tradeoffs inherent in using synthetic data. We will see and discuss those tradeoffs too. Is synthetic data ready for production and how does it compare with real in object detection? Let’s find out. (header image source)

We continue the series on synthetic data for object detection. Last time, we stopped in 2016, with some early works on synthetic data for deep learning that still have implications relevant today. This time, we look at a couple of more recent papers devoted to multiple object detection for food and small vendor items. As we will see today, such objects are a natural application for synthetic data, and we’ll see how this application has evolved in the last few years.

Today, I continue the series on synthetic data for object detection. In the first post of the series, we discussed the object detection problem itself and real world datasets for it, and the second was devoted to popular synthetic datasets of common objects. The time has come to put this data in practice: in this and subsequent posts, we will discuss common contemporary object detection architectures and see how adding synthetic data fares for object detection as reported in literature. In each post, I will give a detailed account of one paper that stands out in my opinion and briefly review one or two more. We begin in 2015.

In the last post, we started talking about object detection. We discussed what the problem is, saw the three main general-purpose real-world datasets for object detection, and began talking about synthetic data. Today, we continue the series with a brief overview of the most important synthetic datasets for object detection. Last time, I made an example of an autonomous driving dataset, but this is a topic of its own, and so are, say, synthetic images of people and human faces. Today, we will concentrate on general-purpose and household object datasets.

Today, we begin a new mini-series that marks a slight change in the direction of the series. Previously, we have talked about the history of synthetic data (one, two, three, four) and reviewed a recent paper on synthetic data. This time, we begin a series devoted to a specific machine learning problem that is often supplemented by the use of synthetic data: object detection. In this first post of the series, we will discuss what the problem is and where the data for object detection comes from and how you can get your network to detect bounding boxes like below (image source).