Datasets:
| 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. | |