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GIGO, Garbage In, Garbage Out: An Urban Garbage Classification Dataset

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13833)


This paper presents a real-world domain-specific dataset, which facilitates algorithm development and benchmarking on the challenging problem of multimodal classification of urban waste in street-level imagery. The dataset, which we have named “GIGO: Garbage in, Garbage out,” consists of 25k images collected from a large geographic area of Amsterdam. It is created with the aim of helping cities to collect different types of garbage from the streets in a more sustainable fashion. The collected data differs from existing benchmarking datasets, introducing unique scientific challenges. In this fine-grained classification dataset, the garbage categories are visually heterogeneous with different sizes, origins, materials, and visual appearance of the objects of interest. In addition, we provide various open data statistics about the geographic area in which the images were collected. Examples are information about demographics, different neighborhood statistics, and information about buildings in the vicinity. This allows for experimentation with multimodal approaches. Finally, we provide several state-of-the-art baselines utilizing the different modalities of the dataset.

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Correspondence to Maarten Sukel .

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Sukel, M., Rudinac, S., Worring, M. (2023). GIGO, Garbage In, Garbage Out: An Urban Garbage Classification Dataset. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham.

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  • Print ISBN: 978-3-031-27076-5

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