EfficientSAM3

Why EfficientSAM3?

SAM3 delivers Promptable Concept Segmentation (PCS) by combining semantic understanding and temporal tracking, yet its massive backbone and dense memory bank make on-device deployment impractical. EfficientSAM3 compresses SAM1, SAM2, and SAM3 into a family of lightweight student models tailored for edge hardware without sacrificing PCS quality.

Updates

2026-01-11: Stage 1 geometry-prompt fine-tuned (ft) weights released/updated (image encoders on 1% SA-1B; text encoders fine-tuned on SA-Co Gold+Silver).
2025-12-08: Stage 1 text encoder weights released for all 3 variants (MobileCLIP S0, S1, and MobileCLIP2 L) - distilled on 1% Recap-DataComp-1B dataset.
2025-12-02: Stage 1 image encoder weights released for all 9 variants (RepViT, TinyViT, EfficientViT) - distilled on 1% of SA-1B dataset. Available via Google Drive and Hugging Face.
2025-10-18: Project announced.

Highlights

Image encoders distilled into RepViT, TinyViT, and EfficientViT families.
Text encoders distilled into MobileCLIP variants (up to 87.96% smaller than SAM3's 354M text encoder), with SA-Co Gold+Silver fine-tuned (ft) weights.
Perceiver-based memory compression aligned with SAM2 temporal tracking.
ONNX/CoreML support for real-time mobile, embedded, and desktop deployment.

Resources

Project README (installation, inference, training)
Dataset setup guide
arXiv

Abstract

SAM3 brought promptable concept segmentation to production scale, but its computational footprint blocks latency-sensitive applications. EfficientSAM3 progressively distills SAM3 into lightweight architectures that maintain PCS quality on edge devices.

We employ a three-stage curriculum: (1) encoder distillation on SA-1B with prompt-in-the-loop supervision, (2) temporal memory distillation on SA-V using a compact Perceiver module, and (3) end-to-end fine-tuning on official SAM3 concept segmentation data. The resulting students deliver real-time segmentation, tracking, and prompt handling on resource-constrained platforms.

Three-Stage Progressive Distillation

Stage 1 · Compact Encoder

Align nine student backbones (RepViT, TinyViT, EfficientViT) with the SAM3 encoder using SA-1B and prompt-in-the-loop supervision.

Stage 2 · Temporal Memory

Compress SAM3's dense video memory into a Perceiver-based module distilled on SA-V, enabling efficient multi-frame reasoning.

Stage 3 · Promptable PCS

Jointly fine-tune encoder, memory, and decoder on SAM3 data to preserve promptable concept segmentation quality.

tl;dr: Stage 1 distills encoder on SAM1 data · Stage 2 aligns memory on SAM2 data · Stage 3 fine-tunes PCS on SAM3 data.

Get Started

Installation

pip install -e ".[stage1]"

See the installation guide for full setup instructions.

Quick Start

# Image prompt
from sam3.model_builder import build_efficientsam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor

# Load model
model = build_efficientsam3_image_model(
  checkpoint_path="efficient_sam3_efficientvit_s.pt",
  backbone_type="efficientvit",
  model_name="b0",
  enable_inst_interactivity=True,
)

# Process image and predict
processor = Sam3Processor(model)
inference_state = processor.set_image(image)

# Single positive point prompt (x, y) in pixels
points = [[image.size[0] / 2, image.size[1] / 2]]
labels = [1]
masks, scores, _ = model.predict_inst(
    inference_state, 
    point_coords=points, 
    point_labels=labels
)

from sam3.model_builder import build_efficientsam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor

# Load model with text encoder
model = build_efficientsam3_image_model(
    checkpoint_path="efficient_sam3_tinyvit_m_mobileclip_s1.pt",
    backbone_type="tinyvit",
    model_name="11m",
    text_encoder_type="MobileCLIP-S1"
)

# Process image and predict with text prompt
processor = Sam3Processor(model)
inference_state = processor.set_image(image)
inference_state = processor.set_text_prompt(prompt="shoe", state=inference_state)
masks = inference_state["masks"]
scores = inference_state["scores"]
print(len(scores), scores)

See image example and text prompt example for details.

EfficientSAM3 Model Zoo & Weight Release

Stage 1 image encoder weights (distilled from SAM3 image encoder) and text encoder weights (distilled from SAM3 text encoder) are now available via Google Drive and Hugging Face. Stage 1 fine-tuned (ft) weights are also available. Stage 2 and 3 weights coming soon.

Image Encoder Models

Model	Backbone	Parameters	Stage 1	Stage 2	Stage 3
ES-RV-S	RepViT-M0.9	4.72M	GDrive / HF	Planned	Planned
ES-RV-M	RepViT-M1.1	7.77M	GDrive / HF (ft: GDrive, HF)	Planned	Planned
ES-RV-L	RepViT-M2.3	22.40M	GDrive / HF	Planned	Planned
ES-TV-S	TinyViT-5M	5.07M	GDrive / HF	Planned	Planned
ES-TV-M	TinyViT-11M	10.55M	GDrive / HF (ft: GDrive, HF)	Planned	Planned
ES-TV-L	TinyViT-21M	20.62M	GDrive / HF	Planned	Planned
ES-EV-S	EfficientViT-B0	0.68M	GDrive / HF	Planned	Planned
ES-EV-M	EfficientViT-B1	4.64M	GDrive / HF (ft: GDrive, HF)	Planned	Planned
ES-EV-L	EfficientViT-B2	14.98M	GDrive / HF	Planned	Planned

Note (2025/12/02): The current Stage 1 image encoder weights are distilled on 1% of the SA-1B dataset.

Note (2026/01/11): The fine-tuned (ft) image models use geometry-prompt fine-tuning on the same 1% subset of SA-1B.

EfficientSAM3 Text Encoder + Image Encoder Models

Model	Backbone	Parameters	Stage 1	Stage 2	Stage 3
ES-RV-S-MC-S1	RepViT-M0.9 & MobileCLIP-S1	4.72M + 63.56M	GDrive / HF	Planned	Planned
ES-RV-M-MC-S1	RepViT-M1.1 & MobileCLIP-S1	7.77M + 63.56M	GDrive / HF (ft: GDrive, HF)	Planned	Planned
ES-RV-L-MC-S1	RepViT-M2.3 & MobileCLIP-S1	22.40M + 63.56M	GDrive / HF	Planned	Planned
ES-TV-S-MC-S1	TinyViT-5M & MobileCLIP-S1	5.07M + 63.56M	GDrive / HF	Planned	Planned
ES-TV-M-MC-S1	TinyViT-11M & MobileCLIP-S1	10.55M + 63.56M	GDrive / HF (ft: GDrive, HF)	Planned	Planned
ES-TV-L-MC-S1	TinyViT-21M & MobileCLIP-S1	20.62M + 63.56M	GDrive / HF	Planned	Planned
ES-EV-S-MC-S1	EfficientViT-B0 & MobileCLIP-S1	0.68M + 63.56M	GDrive / HF	Planned	Planned
ES-EV-M-MC-S1	EfficientViT-B1 & MobileCLIP-S1	4.64M + 63.56M	GDrive / HF (ft: GDrive, HF)	Planned	Planned
ES-EV-L-MC-S1	EfficientViT-B2 & MobileCLIP-S1	14.98M + 63.56M	GDrive / HF	Planned	Planned

Note (2025/12/08): The current Stage 1 text encoder weights are distilled on 1% of the Recap-DataComp-1B dataset.

Note (2026/01/11): Fine-tuned (ft) text encoder models are fine-tuned on SA-Co Gold+Silver text annotations. Standalone fine-tuned text encoder weights: MobileCLIP-S0, MobileCLIP-S1, and MobileCLIP2-L.

SAM3-LiteText Models

SAM3-LiteText replaces the SAM3 text encoder with a lightweight distilled text encoder, reducing text encoder parameters by up to 88% with comparable performance. See the SAM3-LiteText paper for details.

Model	Text Encoder	Ctx	Text Params	Weights
SAM3-LiteText-S0-16	MobileCLIP-S0	16	42.54M	GDrive / HF
SAM3-LiteText-S1-16	MobileCLIP-S1	16	63.53M	GDrive / HF
SAM3-LiteText-L-16	MobileCLIP2-L	16	123.80M	GDrive / HF

Note: All SAM3-LiteText models use the SAM3 ViT-H image encoder (353.72M vision params). The text encoder parameters shown represent the distilled student replacing the original 353.72M text encoder.

Note (2026/02/18): SAM3-LiteText released! SAM3-LiteText reduces text encoder parameters by up to 88% with similar performance to the original text encoder. Paper available on arXiv.

Datasets

Dataset preparation scripts for COCO, DAVIS, LVIS, SA-1B, SA-V, LVOS, MOSE, and YouTube-VOS are located under data/download_*.sh. Refer to README_dataset.md for detailed instructions.

Export & Deployment

ONNX and CoreML export pipelines are under development to unlock mobile and cross-platform deployment. Follow the repository issues for progress updates.

Roadmap

✓ Completed Release Stage 1 image encoder weights (distilled from SAM3 image encoder)
✓ Completed Release Stage 1 text encoder weights (distilled from SAM3 text encoder to MobileCLIP-S1 combined with all 9 image encoder variants)
✓ Completed Release Stage 1+ fine-tuned (ft) encoder weights (geometry-prompt fine-tuning; SA-Co Gold+Silver text fine-tuning)
✓ Completed Release SAM3-LiteText weights (distilled lightweight text encoder competitive with SAM3 text encoder)
Planned Release Stage 2 memory bank aligned models
Planned Release Stage 3 fine-tuned PCS models
Planned ONNX/CoreML export
Planned Interactive web demo

Call for Contributions

We welcome pull requests across the ecosystem:

Efficient MedSAM3 integration and medical datasets
Gradio demos, Vercel deployments, and Hugging Face Spaces
Annotation tool support (X-AnyLabeling, AnyLabeling)
iOS, Android, and NVCC-based desktop applications

Users

Organizations and projects using EfficientSAM3:

European Space Agency

Note: If you're using EfficientSAM3 in your work, please acknowledge us in your publications or projects. We're happy to promote your work here! Contact us to be featured in this section.

Citation

@misc{zeng2025efficientsam3progressivehierarchicaldistillation,
      title={EfficientSAM3: Progressive Hierarchical Distillation for Video Concept Segmentation from SAM1, 2, and 3}, 
      author={Chengxi Zeng and Yuxuan Jiang and Aaron Zhang},
      year={2025},
      eprint={2511.15833},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.15833}, 
}

@misc{zeng2026sam3litetextanatomicalstudysam3,
      title={SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation}, 
      author={Chengxi Zeng and Yuxuan Jiang and Ge Gao and Shuai Wang and Duolikun Danier and Bin Zhu and Stevan Rudinac and David Bull and Fan Zhang},
      year={2026},
      eprint={2602.12173},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.12173}, 
}

EfficientSAM3: Progressive Hierarchical Knowledge Distillation from SAM1, SAM2 & SAM3