to expand the categories (“horse” is expanded to “jumping horse”, “racing horse” ) and the models were also directly trained from the web. The results are shown in Table \[tab:uns\]. For our approach, we try five different settings: 1) GoogleO: Features are based on GoogleO CNN and the bounding boxes are also extracted only on easy Google images; 2) GoogleA: Using GoogleO to extract features instead; 3) FlickrG: Features are based on FlickrG instead; 4) FlickrG-EA: The same Flickr features are used but with EdgeBox augmentation; 5) FlickrG-CE: The Flickr features are used but the positive data includes examples from both original and expanded categories. From the results, we can see that in all cases the CNN based detector boosts the performance a lot.

This demonstrates that our framework could be a powerful way to learn detectors for arbitrary object categories without labeling any training images. We plan to release a service for everyone to train R-CNN detectors on the fly. The code will also be released.

![image](fp){width="0.95\linewidth"}

Failure Modes for Webly Trained Detectors\[sec:diag\]
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In this section, we would like to gain more insights about the potential issues of our webly supervised object detection pipeline. We took the results 