Work todate has highlighted the challenges inmitigating bias within these datasets.

It is crucial that image datasets for computer vision are representative and contain accurate demographic information to ensure their robustness and fairness, especially for smaller subpopulations. To address this issue, we present Dollar Street - a supervised dataset that contains 38,479 images of everyday household items from homes around the world. This dataset was manually curated and fully labeled, including tags for objects (e.g.

The original questions are in bold . The subtext to each question is in italics. The answers are in plain text with no formatting. The questions in this section are primarily intended to encourage dataset creators to clearly articulate their reasons for creating the dataset and to promote transparency about funding interests. For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? The Dollar Street dataset is a supervised image dataset derived from Gapminder's Dollar Street project ( https://www.gapminder.org/dollar-street) that contains everyday household items from homes around the world. It was created with three goals in mind: 1. Make available a highly curated set of images with valuable metadata (e.g. Our evaluation results show that the Dollar Street dataset can add significant value to accuracy improvements when considering computer vision images that represent the geographic and socioeconomic diversity of the world. Concretely, this means that the dataset only contains CC-BY-licensed works.

This dataset includes images from homes with no internet access and incomes as low as $26.99 per

It is crucial that image datasets for computer vision are representative and contain accurate demographic information to ensure their robustness and fairness, especially for smaller subpopulations. To address this issue, we present Dollar Street - a supervised dataset that contains 38,479 images of everyday household items from homes around the world. This dataset was manually curated and fully labeled, including tags for objects (e.g. This dataset includes images from homes with no internet access and incomes as low as \ 26.99 per month, visually capturing valuable socioeconomic diversity of traditionally under-represented populations. All images and data are licensed under CC-BY, permitting their use in academic and commercial work.