Papers › SAM 2: Segment Anything in Images and Videos
SAM 2: Segment Anything in Images and Videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-yuan Wu, Ross Girshick, Piotr Dollár, Christoph Feichtenhofer
We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks. In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM). We believe that our data, model, and insights will serve as a significant milestone for video segmentation and related perception tasks. We are releasing our main model, dataset, as well as code for model training and our demo.
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Code
Syntology Ran 28 of 49 code samples harvested from 6 repositories linked to this paper; 21 have no recorded run. Of those that ran: 2 ran · honoured contract; 4 ran · our draft was wrong; 22 ran with no contract checked.
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Code Syntology ran Syntology
49 samples harvested; 28 ran; 2 honoured the contract we drafted; 21 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | SAM2 | J&F | 90.7 | #1 of 81 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2017 (val) | SAM2 | Params(M) | 224.4 | #1 of 81 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | MOSE | SAM2 | J&F | 77.9 | #1 of 17 | Archive leaderboard | report |
| Video Object Segmentation | M$^3$-VOS | SAM 2 | Average IOU | 69.5 | #4 of 4 | Archive leaderboard | report |
| Visual Object Tracking | DiDi | SAM2.1 | Tracking quality | 0.649 | #3 of 11 | Archive leaderboard | report |
| Visual Object Tracking | VOT2022 | SAM2.1 | EAO | 0.692 | #2 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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