---
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.