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README.md

@@ -1,19 +1,11 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: mask
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 269160334
-    num_examples: 10553
-  - name: test
-    num_bytes: 138830657
-    num_examples: 5019
-  download_size: 409047495
-  dataset_size: 407990991
+license: cc-by-nc-4.0
+pretty_name: DUTS
+task_categories:
+- image-segmentation
+tags:
+- saliency-detection
+- salient-object-detection
 configs:
 - config_name: default
   data_files:
@@ -22,3 +14,38 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# DUTS
+
+DUTS is a large-scale saliency detection (salient object detection) dataset. It
+contains a training set, **DUTS-TR** (10,553 images), and a test set,
+**DUTS-TE** (5,019 images). Each image is paired with a binary ground-truth
+saliency mask.
+
+## Splits
+
+| Split | Source   | Rows   |
+|-------|----------|--------|
+| train | DUTS-TR  | 10,553 |
+| test  | DUTS-TE  | 5,019  |
+
+## Columns
+
+- `image`: the RGB input image (`datasets.Image`).
+- `mask`: the ground-truth saliency mask (`datasets.Image`, single channel).
+
+Image and mask are matched by filename stem.
+
+## License
+
+Released for academic / research use. No explicit SPDX license is provided by
+the authors; this mirror is published under `cc-by-nc-4.0`. See
+<https://saliencydetection.net/duts/> for the original terms.
+
+## Credits
+
+Source: <https://saliencydetection.net/duts/>
+
+Paper: Lijun Wang, Huchuan Lu, Yifan Wang, Mengyang Feng, Dong Wang, Baocai Yin,
+Xiang Ruan. *Learning to Detect Salient Objects with Image-level Supervision.*
+CVPR 2017.