The Berkeley Segmentation Dataset (BSDS500) is a widely used benchmark for image boundary detection and image segmentation. It contains 500 natural images (an extension of the earlier BSDS300) split into train/val/test (200 / 100 / 200). Each image has multiple human-labeled ground-truth segmentations (typically ~5 annotations per image) which are used as reference boundaries/segmentations for evaluation. The dataset is commonly used for contour/boundary detection and region segmentation research; standard evaluation measures include precision/recall on detected boundaries and summary F-measures (e.g., ODS/OIS) and PR curves. The dataset and benchmark resources (download, code, evaluation scripts and leaderboards) are hosted by the UC Berkeley Vision Group.
ODS is the reported evaluation metric for BSDS500. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.
Higher is better
| Rank | Model | Trust | Score | Year | Source |
|---|---|---|---|---|---|
| 01 | Segment Anything Model (SAM) | paper | 0.77 | N/A | Source ↗ |