README.md

metadata

license: cc-by-nc-4.0
pretty_name: COIFT
task_categories:
  - image-segmentation
tags:
  - interactive-segmentation
  - thin-object-segmentation
  - foreground-segmentation
size_categories:
  - n<1K

COIFT

COIFT (COco Instances For Thin objects) is an interactive/thin-object segmentation benchmark consisting of 280 images with high-quality binary foreground masks. It is used to evaluate segmentation of objects with thin structures.

Dataset structure

• Split: test (280 examples) — COIFT is a single benchmark set with no train/test split.
• Columns:
  • image: the RGB input image (datasets.Image).
  • mask: the binary ground-truth foreground mask, single-channel (datasets.Image).

Images and masks are aligned 1:1 by filename stem.

Source & credit

Redistributed from the thin-object-selection repository accompanying the paper "Deep Interactive Thin Object Selection" (Liew et al.).

• Repository: https://github.com/liewjunhao/thin-object-selection

License

Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC-4.0), following the source repository. Non-commercial use only.