mask2former-swin-small-cityscapes-semantic
facebook/mask2former-swin-small-cityscapes-semantic
Tracked by OpenModelStats since September 8, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 806
- Total downloadsCumulative all-time download counter reported by the source platform.
- 49K
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
- +173(increase)
- 0.4%(up) vs baseline
- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
- —
- LikesCommunity likes reported by the source platform.
- 3
- +0 in 7D
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #154 among tracked Image Segmentation models.
- #36,188
- 256(up 256 places)this week
- Parameters
- 68.8M
- Last source update
- Sep 11, 2023
- MomentumOpenModelStats' composite rising-model signal (0–100): a percentile blend of absolute 7-day download gains, capped percentage growth, and likes gained. See Methodology.
- 35.2
- mask2former-swin-small-cityscapes-semantic gained 173 downloads during the last seven tracked days.
- It moved from #153 to #154 among tracked Image Segmentation models this week.
Downloads over time
Rolling recent-window downloads as reportedLikes over time
Rank among tracked models
Lower is betterOverview
mask2former-swin-small-cityscapes-semantic is a image segmentation model published by facebook. OpenModelStats has tracked the model since Sep 8, 2026, most recently observing it 28h ago.
- Published
- Jan 5, 2023
- Last updated
- Sep 11, 2023
- Library
- transformers
- License
- other
- Task
- Image Segmentation
- Parameters
- 68,781,514
- Spaces
- 0
- Derivative models
- —
- Velocity 7D/day
- 25
transformerspytorchsafetensorsmask2formervisionimage-segmentationdataset:cocoarxiv:2112.01527arxiv:2107.06278license:otherendpoints_compatibledeploy:azure
Publisher
facebookView publisher statistics →Similar models
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