these networks (in fact, deeper networks would often need more data to train). But more data needs more human labeling efforts. But data labeling in terms of bounding boxes can be very cumbersome and expensive. Therefore, if we can exploit web data for training CNNs, it would help us move from million to billion image datasets in the future. In this paper, we take the first step in demonstrating: 1) CNNs can be trained effectively by just exploiting web data at much larger scales; 2) competitive object detection results can be obtained without using a single bounding box labels from humans.

![image](approach){width="1.0\linewidth"}

Related Work
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Mining high-quality visual data and learning good visual representation for recognition from the web naturally form two aspects of a typical chicken-and-egg problem in vision. On one hand, clean and representative seed images can help build better and more powerful models; but on the other hand, models that recognize concepts well are crucial for indexing and retrieving image sets that contain the concept of interest. How to attack this problem has long been attractive to both industry and academia.

**From Models to Data**: Image retrieval [@smeulders2000content; @sivic2003video] is a classical problem in this setting. It 