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README.md

@@ -1,19 +1,14 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 1498379344
-    num_examples: 5248
-  - name: test
-    num_bytes: 148427155
-    num_examples: 500
-  download_size: 1647183668
-  dataset_size: 1646806499
+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:
@@ -22,3 +17,38 @@ configs:
   - 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.