Datasets › UTFPR-SBD3

UTFPR-SBD3

1 Nov 2021 archive 2025-07-28

The semantic segmentation of clothes is a challenging task due to the wide variety of clothing styles, layers and shapes. The UTFPR-SBD3 contains 4,500 images manually annotated at pixel level in 18 classes plus background. To ensure the high quality of the dataset, all images were manually annotated at the pixel level using JS Segment Annotator, 2 a free web-based image annotation tool. The raw images were carefully selected to avoid, as far as possible, classes with low number of instances.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Semantic Segmentation UTFPR-SBD3 EPYNET 1:1 Accuracy 92,06 EPYNET: Efficient Pyramidal Network for Clothing Segmentation — 1 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 1. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
EPYNET: Efficient Pyramidal Network for Clothing Segmentation 0 1 13 Oct 2020 not harvested

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • UTFPR-SBD3

1 variant name, as the archive lists them.

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