Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published June 2015 | metadata_only
Book Section - Chapter

Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection


We introduce tools and methodologies to collect high quality, large scale fine-grained computer vision datasets using citizen scientists - crowd annotators who are passionate and knowledgeable about specific domains such as birds or airplanes. We worked with citizen scientists and domain experts to collect NABirds, a new high quality dataset containing 48,562 images of North American birds with 555 categories, part annotations and bounding boxes. We find that citizen scientists are significantly more accurate than Mechanical Turkers at zero cost. We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%. Nevertheless, we found that learning algorithms are surprisingly robust to annotation errors and this level of training data corruption can lead to an acceptably small increase in test error if the training set has sufficient size. At the same time, we found that an expert-curated high quality test set like NABirds is necessary to accurately measure the performance of fine-grained computer vision systems. We used NABirds to train a publicly available bird recognition service deployed on the web site of the Cornell Lab of Ornithology.

Additional Information

© 2015 IEEE. We would like to thank Nathan Goldberg, Ben Barkley, Brendan Fogarty, Graham Montgomery, and Nathaniel Hernandez for assisting with the user experiments. We appreciate the feedback and general guidance from Miyoko Chu, Steve Kelling, Chris Wood and Alex Chang. This work was supported in part by a Google Focused Research Award, the Jacobs Technion-Cornell Joint Research Fund, and Office of Naval Research MURI N000141010933.

Additional details

September 15, 2023
September 15, 2023