---
license: cc-by-nc-4.0
pretty_name: DAVIS-S
task_categories:
  - image-segmentation
tags:
  - saliency
  - salient-object-detection
  - segmentation
  - high-resolution
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
---

# DAVIS-S

DAVIS-S is a high-resolution salient object detection (SOD) evaluation set,
released alongside the **HRSOD** (High-Resolution Salient Object Detection)
paper. It is a saliency-annotated subset of the **DAVIS** video object
segmentation dataset, curated to benchmark high-resolution salient object
detection methods. Images are full-HD (1920x1080) with pixel-accurate
binary ground-truth saliency masks.

## Dataset structure

- **Split:** `test` (single evaluation set), 92 image/mask pairs.
- **Columns:**
  - `image`: the RGB source image (`datasets.Image`).
  - `mask`: the grayscale ground-truth saliency mask (`datasets.Image`).

## Usage

```python
from datasets import load_dataset

ds = load_dataset("nobg/DAVIS-S", split="test")
ex = ds[0]
ex["image"]  # PIL.Image, RGB, 1920x1080
ex["mask"]   # PIL.Image, L (grayscale) saliency mask
```

## Source & credits

- **DAVIS dataset** — the underlying images originate from the DAVIS
  (Densely Annotated VIdeo Segmentation) benchmark.
- **HRSOD authors** — the high-resolution saliency subset and ground-truth
  masks were released as part of the HRSOD project
  ([yi94code/HRSOD](https://github.com/yi94code/HRSOD)).

Please cite the DAVIS and HRSOD works if you use this dataset.

## License

Released under **CC BY-NC 4.0** (non-commercial research use), consistent
with the DAVIS dataset licensing.