vision
EfficientViT-SAM
MIT Han Lab Segment Anything with an EfficientViT image encoder for faster inference.
Levelmodel
Classvision
Parametersnot in the notes
Contextnot in the notes
Released7 Feb 2024
Opennessopen weights permissive
Runs onnot in the notes
Statusactive
R&D Topics
Identity
- Developer
- MIT Han Lab
- Family
- not in the notes
- Released
- 7 Feb 2024 sourcesannouncement page
- Weights published
- 30 Sep 2023
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- dense transformer
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- image
- Output
- image
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open weights permissive
- Licence
- Apache License 2.0 sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperMIT Han Labuniversity, USA
MIT Han Labuniversity, USAModelEfficientViT-SAMMIT Han Lab, vision
EfficientViT-SAMMIT Han Lab, visionR&D TopicsPromptable Segmentation Foundation Models (Segment Anything)Efficient Vision Networks for Mobile and Edge Devices
Use CasesMedical Image SegmentationHealthcareUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs Itnone recordedDeployments of these use cases; none names this model.
Organisations 1
R&D topics 2
Efficient Vision Networks for Mobile and Edge DevicesSmall, fast vision networks, often found by architecture search, that run on phones, came…produced byPromptable Segmentation Foundation Models (Segment Anything)Models that cut out any object from a point, box or text prompt, trained on very large ma…produced by
Sources
- arxiv.org/abs/2402.05008 https://arxiv.org/abs/2402.05008
- huggingface.co/mit-han-lab/efficientvit-sam https://huggingface.co/mit-han-lab/efficientvit-sam
- huggingface.co/api/models?pipeline_tag=mask-generation&sort=... https://huggingface.co/api/models?pipeline_tag=mask-generation&sort=downloads&limit=50
- huggingface.co/yonigozlan/EdgeTAM-hf https://huggingface.co/yonigozlan/EdgeTAM-hf
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "EfficientViT-SAM", https://aurion.semai.id/models/mit-han-lab-efficientvit-sam/, records of October 2026.