neurovfm-encoder
mlinslab/neurovfm-encoder
Tracked by OpenModelStats since August 24, 2026View on Hugging Face ↗
- Downloads 30DRolling recent-window download count as reported by the source platform.
- 1,378
- Total downloadsCumulative all-time download counter reported by the source platform.
- 8,350
- Gained 7DIncrease in cumulative total downloads between OpenModelStats' own observations seven days apart.
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- Gained 30DIncrease in cumulative total downloads between OpenModelStats' own observations roughly thirty days apart.
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- LikesCommunity likes reported by the source platform.
- 16
- Rank · tracked modelsPosition among all eligible models tracked by OpenModelStats, ordered by recent downloads. #212 among tracked Image Feature Extraction models.
- #21,535
- 19249(down 19249 places)this week
- Parameters
- —
- Last source update
- Jul 10, 2026
- It moved from #17 to #212 among tracked Image Feature Extraction models this week.
Downloads over time
Cumulative total downloads observed by OpenModelStatsNot enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Likes over time
Not enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Rank among tracked models
Lower is betterNot enough tracked history yet for a chart.
OpenModelStats began tracking this model on 2026-08-24. Charts appear as daily observations accumulate.
Overview
neurovfm-encoder is a image feature extraction model published by mlinslab. OpenModelStats has tracked the model since Aug 24, 2026, most recently observing it 3h ago.
- Published
- Nov 6, 2025
- Last updated
- Jul 10, 2026
- Library
- —
- License
- cc-by-nc-sa-4.0
- Task
- Image Feature Extraction
- Parameters
- —
- Spaces
- —
- Derivative models
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- Velocity 7D/day
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pytorchmedicalneuroimagingmrictfoundation-modelself-supervisedjepaimage-feature-extractionlicense:cc-by-nc-sa-4.0region:us
Publisher
mlinslabView publisher statistics →Similar models
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