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SlimSAM: 0.1% Data Makes Segment Anything Slim

0.1% Data Makes Segment Anything Slim
Zigeng Chen, Gongfan Fang, Xinyin Ma, Xinchao Wang
Learning and Vision Lab, National University of Singapore
Paper: [Arxiv]

Introduction

SlimSAM is a novel SAM compression method, which efficiently reuses pre-trained SAMs without the necessity for extensive retraining. This is achieved by the efficient reuse of pre-trained SAMs through a unified pruning-distillation framework. To enhance knowledge inheritance from the original SAM, we employ an innovative alternate slimming strategy that partitions the compression process into a progressive procedure. Diverging from prior pruning techniques, we meticulously prune and distill decoupled model structures in an alternating fashion. Furthermore, a novel label-free pruning criterion is also proposed to align the pruning objective with the optimization target, thereby boosting the post-distillation after pruning.

Frame

SlimSAM achieves approaching performance while reducing the parameter counts to 0.9% (5.7M), MACs to 0.8% (21G), and requiring mere 0.1% (10k) of the training data when compared to the original SAM-H. Extensive experiments demonstrate that our method realize significant superior performance while utilizing over 10 times less training data when compared to other SAM compression methods.

Visualization Results

Qualitative comparison of results obtained using point prompts, box prompts, and segment everything prompts are shown in the following section.

Segment Everything Prompts

Box Prompts and Point Prompts

Quantitative Results

We conducted a comprehensive comparison encompassing performance, efficiency, and training costs with other SAM compression methods and structural pruning methods.

Comparing with other SAM compression methods.

Comparing with other structural pruning methods.

Model Using

Fast state_dict loading for local uniform pruning SlimSAM-50 model:

model = SamModel.from_pretrained("Zigeng/SlimSAM-uniform-77").to("cuda")
processor = SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-77")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window
inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda")
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
scores = outputs.iou_scores

BibTex of our SlimSAM

If you use SlimSAM in your research, please use the following BibTeX entry. Thank you!

@misc{chen202301,
      title={0.1% Data Makes Segment Anything Slim}, 
      author={Zigeng Chen and Gongfan Fang and Xinyin Ma and Xinchao Wang},
      year={2023},
      eprint={2312.05284},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgement

SAM (Segment Anything) [bib]
@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}
}
Torch Pruning (DepGraph: Towards Any Structural Pruning) [bib]
@inproceedings{fang2023depgraph,
  title={Depgraph: Towards any structural pruning},
  author={Fang, Gongfan and Ma, Xinyin and Song, Mingli and Mi, Michael Bi and Wang, Xinchao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16091--16101},
  year={2023}
}
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