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arxiv:2404.08639

COCONut: Modernizing COCO Segmentation

Published on Apr 12
ยท Submitted by akhaliq on Apr 15
#3 Paper of the day
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Abstract

In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Notably, the established COCO benchmark has propelled the development of modern detection and segmentation systems. However, the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. Originally equipped with coarse polygon annotations for thing instances, it gradually incorporated coarse superpixel annotations for stuff regions, which were subsequently heuristically amalgamated to yield panoptic segmentation annotations. These annotations, executed by different groups of raters, have resulted not only in coarse segmentation masks but also in inconsistencies between segmentation types. In this study, we undertake a comprehensive reevaluation of the COCO segmentation annotations. By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5.18M panoptic masks, we introduce COCONut, the COCO Next Universal segmenTation dataset. COCONut harmonizes segmentation annotations across semantic, instance, and panoptic segmentation with meticulously crafted high-quality masks, and establishes a robust benchmark for all segmentation tasks. To our knowledge, COCONut stands as the inaugural large-scale universal segmentation dataset, verified by human raters. We anticipate that the release of COCONut will significantly contribute to the community's ability to assess the progress of novel neural networks.

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edited Apr 19

update: dataset moved to kaggle, here is the the new link: https://www.kaggle.com/datasets/xueqingdeng/coconut/

@xdeng the dataset seems to be gone :(

ยท
Paper author

sorry for the confusion, we moved the dataset moved to kaggle, here is the the new link: https://www.kaggle.com/datasets/xueqingdeng/coconut/

@xdeng77 is there any reason why behind your decision to take it down on Hub?

ยท
Paper author

We figured out that directly using the huggingface api "load_dataset" did not download the json file we attached, without the segments info from the jsonfile then the panoptic segmentation masks can not be used correctly. We are now working on this issue, and should be available on huggingface once it is ready.

COCONut: Revolutionizing COCO with Next-Gen Segmentation Annotations!

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