LVIS (Instance Segmentation).

LVIS is a large-scale, high-quality dataset for instance segmentation containing 160k-164k images and 2M instance annotations for over 1000 object categories. It focuses on long-tail object recognition, providing a larger and more detailed vocabulary than COCO. LVIS uses the same images as the COCO dataset but with different splits and annotations optimized for instance segmentation. The dataset includes common and rare object categories and provides standardized evaluation metrics like mean Average Precision (mAP) for instance segmentation.

Best published scores.

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

Primary

mAP · higher is better

1 steps
of state of the art.

Each row below marks a model that broke the previous record on mAP. 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)44.70

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