| --- |
| 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: |
| - split: train |
| path: data/train-* |
| - 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. |
|
|
