Berkeley Segmentation Dataset (BSDS500).

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.

Best published scores.

1 result indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary

ODS · higher is better

1 steps
of state of the art.

Each row below marks a model that broke the previous record on ODS. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.

Higher scores win. Each subsequent entry improved upon the previous best.

1. Apr 5, 2023Segment Anything Model (SAM)0.768

1 paper
tied to this benchmark.

Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.

Neighbouring benchmarks.

Other Image segmentation datasets on Codesota.

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

• 01A public checkpoint or API endpoint
• 02A reproduction script with frozen commit + seed
• 03Declared evaluation environment (Python, deps)
• 04One row per metric declared by this dataset
• 05A contact so we can follow up on discrepancies