EfficientSAM

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

News

[Jan.12 2024] ONNX version of EfficientSAM including separate encoder and decoder is available on the Hugging Face Space (thanks to @wkentaro Kentaro Wada for implementing onnx export)

[Dec.31 2023] EfficientSAM is integrated into the annotation tool, Labelme (huge thanks to lableme team and @wkentaro Kentaro Wada)

[Dec.11 2023] The EfficientSAM model code with checkpoints is fully available in this repository. The example script shows how to instantiate the model with checkpoint and query points on an image.

[Dec.10 2023] Grounded EfficientSAM demo is available on Grounded-Efficient-Segment-Anything (huge thanks to IDEA-Research team and @rentainhe for supporting grounded-efficient-sam demo under Grounded-Segment-Anything).

[Dec.6 2023] EfficientSAM demo is available on the Hugging Face Space (huge thanks to all the HF team for their support).

[Dec.5 2023] We release the torchscript version of EfficientSAM and share a colab.

Online Demo & Examples

Online demo and examples can be found in the project page.

EfficientSAM Instance Segmentation Examples

Point-prompt
Box-prompt
Segment everything
Saliency

Model

EfficientSAM checkpoints are available under the weights folder of this github repository. Example instantiations and run of the models can be found in EfficientSAM_example.py.

You can directly use EfficientSAM with checkpoints,

from efficient_sam.build_efficient_sam import build_efficient_sam_vitt, build_efficient_sam_vits
efficientsam = build_efficient_sam_vitt()

Jupyter Notebook Example

The notebook is shared here

Acknowledgement

• SAM
• MobileSAM
• FastSAM
• U-2-Net

If you're using EfficientSAM in your research or applications, please cite using this BibTeX:

@article{xiong2023efficientsam,
  title={EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything},
  author={Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra},
  journal={arXiv:2312.00863},
  year={2023}
}