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https://github.com/czg1225/SlimSAM

[NeurIPS 2024] SlimSAM: 0.1% Data Makes Segment Anything Slim
https://github.com/czg1225/SlimSAM

knowledge-distillation model-compression model-pruning segment-anything-model

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[NeurIPS 2024] SlimSAM: 0.1% Data Makes Segment Anything Slim

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SlimSAM: 0.1% Data Makes Segment Anything Slim


  


  

    BLOOM

  

  

    PyTorch>=v1.7.1

  

  

    LLaMA

  

  

    Llama-2

  

  

    Vicuna

  

  

    Vicuna

  






> **0.1% Data Makes Segment Anything Slim**   

> [Zigeng Chen](https://github.com/czg1225), [Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)   

> [Learning and Vision Lab](http://lv-nus.org/), National University of Singapore  

> Paper: [[Paper]](https://arxiv.org/abs/2312.05284)



### Updates

* 🚀 **March 22, 2024**: Awesome-Efficient-Segment-Anything is now available. Find more efficient SAMs [here](https://github.com/czg1225/Awesome-Efficient-Segment-Anything).

* 🚀 **January 10, 2024**: Run **SlimSAM** in your browser with :hugs: Transformers.js ([demo](https://huggingface.co/spaces/Xenova/segment-anything-web)).

* 🚀 **January 9, 2024**: Quickly loading using huggingface :hugs: :hugs: :hugs: .

* 🚀 **January 7, 2024**: Release models using uniform local pruning for easier state dict loading.

* 🚀 **December 19, 2023**: Release the Colab example for **SlimSAM**.

* 🚀 **December 11, 2023**: Release the training code, inference code and pre-trained models for **SlimSAM**. 



![everything](images/paper/everything2.PNG)



## Fast Start 🚀



#### Quickly loading with Huggingface :hugs::



``` python

from PIL import Image

from transformers import SamModel, SamProcessor



model = SamModel.from_pretrained("Zigeng/SlimSAM-uniform-50").to("cuda")

processor = SamProcessor.from_pretrained("Zigeng/SlimSAM-uniform-50")



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

```



## Introduction

















**SlimSAM** is a novel data-efficient SAM compression method that achieves superior performance with extremely less training data. The essence of SlimSAM is encapsulated in the alternate slimming framework which effectively enhances knowledge inheritance under severely limited training data availability and exceptional pruning ratio. Diverging from prior techniques, our framework progressively compresses the model by alternately pruning and distilling distinct, decoupled sub-structures. Disturbed Taylor pruning is also proposed to address the misalignment between the pruning objective and training target, thereby boosting the post-distillation after pruning.



![SlimSAM process](images/paper/frame.png)



SlimSAM yields significant performance improvements while demanding **over 10 times less** training data than any other existing compression methods. Even when compared to the original SAM, SlimSAM achieves approaching performance while reducing parameter counts to merely **1.4\% (9.1M)**, MACs to **0.8\% (23G)**, and requiring only **0.1\% (10k)** of the SAM training data.



## Visualization Results



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

### Box Prompts and Point Prompts



![prompt](images/paper/prompt.PNG)



## 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.



  




## Installation



The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.



Install with



```

pip install -e .

```



The following optional dependencies are necessary for mask post-processing, saving masks in COCO format.



```

pip install opencv-python pycocotools matplotlib 

```



## Dataset

We use the original SA-1B dataset in our code. See [here](https://ai.facebook.com/datasets/segment-anything/) for an overview of the datastet. The dataset can be downloaded [here](https://ai.facebook.com/datasets/segment-anything-downloads/).



The download dataset should be saved as:



```

/

      sa_xxxxxxx.jpg

      sa_xxxxxxx.json

      ......

/

      sa_xxxxxxx.jpg

      sa_xxxxxxx.json

      ......



```



To decode a mask in COCO RLE format into binary:



``` python

from pycocotools import mask as mask_utils

mask = mask_utils.decode(annotation["segmentation"])

```



See [here](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py) for more instructions to manipulate masks stored in RLE format.



## Model Checkpoints



The base model of our method is available. To enhance collaboration with our dependency dectection algorithm, we have split the original image encoder's qkv layer into three distinct linear layers: q, k, and v.








Click the links below to download the checkpoints of orginal SAM-B.



- `SAM-B`: [SAM-B model.](https://drive.google.com/file/d/1CtcyOm4h9bXgBF8DEVWn3N7g9-3r4Xzz/view?usp=sharing)



The check points of our SlimSAM are avalable. We release two versions, which are SlimSAM-50 (pruning ratio = 50%) and SlimSAM-77 (pruning ratio = 77%).



Click the links below to download the checkpoints for the corresponding pruning ratio.



#### Global Pruning Models:



- `SlimSAM-50`: [SlimSAM-50 model.](https://drive.google.com/file/d/1bTjBZs2oWHeo6OPxumD_Gces4VCcU0JI/view?usp=sharing)

- `SlimSAM-77`: [SlimSAM-77 model.](https://drive.google.com/file/d/14BhU66umvY0E1FWoGsCMpLqXMNw9c3Nx/view?usp=sharing)



Above models can be instantiated by running



``` python

import torch

SlimSAM_model = torch.load()

SlimSAM_model.image_encoder = SlimSAM_model.image_encoder.module



def forward(self, x):



    x = self.patch_embed(x)

    if self.pos_embed is not None:

        x = x + self.pos_embed



    for blk in self.blocks:

        x,qkv_emb,mid_emb,x_emb = blk(x)



    x = self.neck(x.permute(0, 3, 1, 2))

    

    return x



import types

funcType = types.MethodType

SlimSAM_model.image_encoder.forward = funcType(forward, SlimSAM_model.image_encoder)

SlimSAM_model.to(device)

SlimSAM_model.eval()

```



#### Local Pruning Models:



- `SlimSAM-50-uniform`: [SlimSAM-50 model.](https://drive.google.com/file/d/1Ld7Q2LY8H2nu4zB6VxwwA5npS5A9OHFq/view?usp=sharing)

- `SlimSAM-77-uniform`: [SlimSAM-77 model.](https://drive.google.com/file/d/1OeWpfk5WhdlMz5VvYmb9gaE6suzHB0sp/view?usp=sharing)



Above models can be instantiated by running



``` python

import torch

from segment_anything import sam_model_registry



model_type = 'vit_p50'

checkpoint = 'checkpoints/SlimSAM-50-uniform.pth'

SlimSAM_model = sam_model_registry[model_type](checkpoint=checkpoint)

SlimSAM_model.to(device)

SlimSAM_model.eval()

```



## Inference



First download [SlimSAM-50 model](https://drive.google.com/file/d/1iCN9IW0Su0Ud_fOFoQUnTdkC3bFveMND/view?usp=sharing) or  [SlimSAM-77 model](https://drive.google.com/file/d/1L7LB6gHDzR-3D63pH9acD9E0Ul9_wMF-/view) for inference



We provide detailed instructions in 'inference.py' on how to use a range of prompts, including 'point' and 'box' and 'everything', for inference purposes.



```

CUDA_VISIBLE_DEVICES=0 python inference.py

```



## Train



First download a [SAM-B model](https://drive.google.com/file/d/1CtcyOm4h9bXgBF8DEVWn3N7g9-3r4Xzz/view?usp=sharing) into 'checkpoints/' as the base model. 



### Step1: Embedding Pruning + Bottleneck Aligning ###

The model after step1 is saved as 'checkpoints/vit_b_slim_step1_.pth'



```

CUDA_VISIBLE_DEVICES=0 python prune_distill_step1.py  --traindata_path  --valdata_path  --prune_ratio  --epochs 

```



### Step2: Bottleneck Pruning + Embedding Aligning ###

The model after step2 is saved as 'checkpoints/vit_b_slim_step2_.pth'



```

CUDA_VISIBLE_DEVICES=0 python prune_distill_step2.py  --traindata_path  --valdata_path  --prune_ratio  --epochs  --model_path 'checkpoints/vit_b_slim_step1_.pth' 



```



You can adjust the training settings to meet your specific requirements. While our method demonstrates impressive performance with just 10,000 training data, incorporating additional training data will further enhance the model's effectiveness



## BibTex of our SlimSAM

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



```bibtex

@article{chen20230,

  title={0.1\% Data Makes Segment Anything Slim},

  author={Chen, Zigeng and Fang, Gongfan and Ma, Xinyin and Wang, Xinchao},

  journal={arXiv preprint arXiv:2312.05284},

  year={2023}

}

```



## Acknowledgement



SAM (Segment Anything) [bib]



```bibtex

@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]



```bibtex

@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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