EfficientSAM3 v0.3.0 — EfficientSAM3.1 & SAM3.1-LiteText Image Model Release

EfficientSAM3 — v3.0 Models & Hugging Face Integration (2026-04-13)

We’re excited to announce the release of the EfficientSAM3.1 and SAM3.1-LiteText models, alongside official Hugging Face integration and advanced data engine support!

What’s new

• New Models Released: EfficientSAM3.1 and SAM3.1-LiteText image models are now available on the stage1_sam3.1 branch.
• Hugging Face Integration: SAM3-LiteText has been officially merged into HuggingFace Transformers.
• Data Engine Support: Stage 3 data engine support is now live on the data_engine branch.
  

🏆 Community Heroes

A massive thank you to the community members who helped make this release possible:

• @NielsRogge, @yonigozlan, and the Hugging Face team: For integrating SAM3-LiteText into the Hugging Face API.
• @colinlin1982: For finding redundant original SAM3 weights in the efficient models and adding a trim script to reduce the model size. (Note: The new EfficientSAM3.1 models do not need to be trimmed).
• @clcl777: For adding multi-device support.

EfficientSAM3 v0.2.0 — SAM3-LiteText

EfficientSAM3 — SAM3-LiteText (2026-02-18)

We’re releasing SAM3-LiteText, a highly efficient text encoder variant for SAM3.

• What’s new

  • SAM3-LiteText released! Reduces text encoder parameters by up to 88% with similar performance to the original text encoder.
  • Paper Released: arXiv:2602.12173 (PDF)
  • Code: Available in the sam3_litetext branch.

• Get the weights

  • Download from Hugging Face: Simon7108528/EfficientSAM3/tree/main/sam3_litetext

• Quick start

  • Checkout the branch: git checkout sam3_litetext
  • Install: pip install -e ".[stage1]"
  • Run inference with the new lite text encoders.

EfficientSAM3 v0.1.1 — Stage 1+ Fine-Tuned (ft) Weights

EfficientSAM3 — Stage 1+ Fine-Tuned (ft) Weights (2026-01-11)

We’re releasing/refreshing Stage 1 geometry-prompt fine-tuned (ft) checkpoints for EfficientSAM3.

• What’s new

  • Image encoder ft weights (trained on 1% SA-1B with geometry-prompt fine-tuning)
  • Text encoder ft weights (fine-tuned on SA-Co Gold + Silver text annotations)
  • Updated download links in the Model Zoo (Google Drive + Hugging Face)

• Get the weights

  • See the Model Zoo in the repo README.md and the project page: https://simonzeng7108.github.io/efficientsam3/

• Quick start

  • Install: pip install -e ".[stage1]"
  • Inference examples are in the repo under sam3/efficientsam3_examples/