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Current dataset collection methods typically scrape large amounts of datafrom the web. While this technique is extremely scalable, data collected inthis way tends to reinforce stereotypical biases, can contain personallyidentifiable information, and typically originates from Europe and NorthAmerica. In this work, we rethink the dataset collection paradigm and introduceGeoDE, a geographically diverse dataset with 61,940 images from 40 classes and6 world regions, and no personally identifiable information, collected throughcrowd-sourcing. We analyse GeoDE to understand differences in images collectedin this manner compared to web-scraping. Despite the smaller size of thisdataset, we demonstrate its use as both an evaluation and training dataset,highlight shortcomings in current models, as well as show improved performanceswhen even small amounts of GeoDE (1000 - 2000 images per region) are added to atraining dataset. We release the full dataset and code athttps://geodiverse-data-collection.cs.princeton.edu/

GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition