Datasets:
license: cc-by-nc-4.0
pretty_name: ThinObject-5K
task_categories:
- image-segmentation
tags:
- thin-object-segmentation
- saliency
- matting
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
ThinObject-5K
ThinObject-5K is a high-resolution dataset for thin object segmentation, containing 5,748 images with pixel-accurate binary ground-truth masks that emphasize thin structures (e.g. wires, legs, handles, wineglass stems, antennae).
Splits
| Split | Rows | Source list |
|---|---|---|
| train | 5248 | official trainval.txt |
| test | 500 | official test.txt |
The two splits are disjoint and together cover all 5,748 image/mask pairs. The original repository additionally provides a train.txt (4,748) / validation (500) partition; the validation subset is folded into the train split here and is recoverable from the original lists if needed.
Schema
| Column | Type | Description |
|---|---|---|
image |
Image |
RGB photograph (JPEG) |
mask |
Image |
Single-channel (mode L) binary ground-truth segmentation mask, same resolution as the image |
Source & Credit
This dataset was introduced in:
Deep Interactive Thin Object Selection Jun Hao Liew, Scott Cohen, Brian Price, Long Mai, Jiashi Feng. WACV 2021.
Original repository: https://github.com/liewjunhao/thin-object-selection
Original data (Google Drive) is redistributed here for convenience. All credit belongs to the original authors. Please cite the paper above when using this dataset.
License
Released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, matching the license of the original thin-object-selection repository. Non-commercial use only.