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Faster Segment Anything (MobileSAM) and Everything (MobileSAMv2)

:pushpin: MobileSAMv2, available at ResearchGate and arXiv, replaces the grid-search prompt sampling in SAM with object-aware prompt sampling for faster segment everything(SegEvery).

:pushpin: MobileSAM, available at ResearchGate and arXiv, replaces the heavyweight image encoder in SAM with a lightweight image encoder for faster segment anything(SegAny).

Support for ONNX model export. Feel free to test it on your devices and share your results with us.

A demo of MobileSAM running on CPU is open at hugging face demo. On our own Mac i5 CPU, it takes around 3s. On the hugging face demo, the interface and inferior CPUs make it slower but still works fine. Stayed tuned for a new version with more features! You can also run a demo of MobileSAM on your local PC.

:grapes: Media coverage and Projects that adapt from SAM to MobileSAM (Thank you all!)

:star: How is MobileSAM trained? MobileSAM is trained on a single GPU with 100k datasets (1% of the original images) for less than a day. The training code will be available soon.

:star: How to Adapt from SAM to MobileSAM? Since MobileSAM keeps exactly the same pipeline as the original SAM, we inherit pre-processing, post-processing, and all other interfaces from the original SAM. Therefore, by assuming everything is exactly the same except for a smaller image encoder, those who use the original SAM for their projects can adapt to MobileSAM with almost zero effort.

:star: MobileSAM performs on par with the original SAM (at least visually) and keeps exactly the same pipeline as the original SAM except for a change on the image encoder. Specifically, we replace the original heavyweight ViT-H encoder (632M) with a much smaller Tiny-ViT (5M). On a single GPU, MobileSAM runs around 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.

:star: Original SAM and MobileSAM with a point as the prompt.

<p float="left"> <img src="assets/mask_point.jpg?raw=true" width="99.1%" /> </p>

:star: Original SAM and MobileSAM with a box as the prompt.

<p float="left"> <img src="assets/mask_box.jpg?raw=true" width="99.1%" /> </p>

:muscle: Is MobileSAM faster and smaller than FastSAM? Yes! MobileSAM is around 7 times smaller and around 5 times faster than the concurrent FastSAM. The comparison of the whole pipeline is summarzed as follows:

Whole Pipeline (Enc+Dec)FastSAMMobileSAM
Parameters68M9.66M
Speed64ms12ms

:muscle: Does MobileSAM aign better with the original SAM than FastSAM? Yes! FastSAM is suggested to work with multiple points, thus we compare the mIoU with two prompt points (with different pixel distances) and show the resutls as follows. Higher mIoU indicates higher alignment.

mIoUFastSAMMobileSAM
1000.270.73
2000.330.71
3000.370.74
4000.410.73
5000.410.73

Installation

The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Install Mobile Segment Anything:

pip install git+https://github.com/ChaoningZhang/MobileSAM.git

or clone the repository locally and install with

git clone git@github.com:ChaoningZhang/MobileSAM.git
cd MobileSAM; pip install -e .

Demo

Once installed MobileSAM, you can run demo on your local PC or check out our HuggingFace Demo.

It requires latest version of gradio.

cd app
python app.py

<a name="GettingStarted"></a>Getting Started

The MobileSAM can be loaded in the following ways:

from mobile_sam import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor

model_type = "vit_t"
sam_checkpoint = "./weights/mobile_sam.pt"

device = "cuda" if torch.cuda.is_available() else "cpu"

mobile_sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
mobile_sam.to(device=device)
mobile_sam.eval()

predictor = SamPredictor(mobile_sam)
predictor.set_image(<your_image>)
masks, _, _ = predictor.predict(<input_prompts>)

or generate masks for an entire image:

from mobile_sam import SamAutomaticMaskGenerator

mask_generator = SamAutomaticMaskGenerator(mobile_sam)
masks = mask_generator.generate(<your_image>)

<a name="GettingStarted"></a>Getting Started (MobileSAMv2)

Download the model weights from the checkpoints.

After downloading the model weights, faster SegEvery with MobileSAMv2 can be simply used as follows:

cd MobileSAMv2
bash ./experiments/mobilesamv2.sh

ONNX Export

MobileSAM now supports ONNX export. Export the model with

python scripts/export_onnx_model.py --checkpoint ./weights/mobile_sam.pt --model-type vit_t --output ./mobile_sam.onnx

Also check the example notebook to follow detailed steps. We recommend to use onnx==1.12.0 and onnxruntime==1.13.1 which is tested.

BibTex of our MobileSAM

If you use MobileSAM in your research, please use the following BibTeX entry. :mega: Thank you!

@article{mobile_sam,
  title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
  author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung-Ho and Lee, Seungkyu and Hong, Choong Seon},
  journal={arXiv preprint arXiv:2306.14289},
  year={2023}
}

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155911, Artificial Intelligence Convergence Innovation Human Resources Development (Kyung Hee University))

<details> <summary> <a href="https://github.com/facebookresearch/segment-anything">SAM</a> (Segment Anything) [<b>bib</b>] </summary>
@article{kirillov2023segany,
  title={Segment Anything}, 
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}
</details> <details> <summary> <a href="https://github.com/microsoft/Cream/tree/main/TinyViT">TinyViT</a> (TinyViT: Fast Pretraining Distillation for Small Vision Transformers) [<b>bib</b>] </summary>
@InProceedings{tiny_vit,
  title={TinyViT: Fast Pretraining Distillation for Small Vision Transformers},
  author={Wu, Kan and Zhang, Jinnian and Peng, Houwen and Liu, Mengchen and Xiao, Bin and Fu, Jianlong and Yuan, Lu},
  booktitle={European conference on computer vision (ECCV)},
  year={2022}
</details>