Update README.md

README.md

@@ -2,7 +2,64 @@
 license: apache-2.0
 ---
 
-## Fast state_dict loading for local uniform pruning SlimSAM-50 model:
+# SlimSAM: 0.1% Data Makes Segment Anything Slim
+<div align="center">
+<img src="images/paper/intro.PNG" width="66%">
+<img src="images/paper/everything.PNG" width="100%">
+</div>
+
+> **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: [[Arxiv]](https://arxiv.org/abs/2312.05284)
+
+## Introduction
+
+<div align="center">
+<img src="images/paper/process.PNG" width="100%">
+</div>
+
+**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](images/paper/frame.PNG?raw=true)
+
+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
+<div align="center">
+  <img src="images/paper/everything2.PNG" width="100%">
+</div>
+
+### Box Prompts and Point Prompts
+<div align="center">
+  <img src="images/paper/prompt.PNG" width="100%">
+</div>
+
+
+## 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.
+<div align="center">
+  <img src="images/paper/compare_tab1.PNG" width="100%">
+</div>
+
+### Comparing with other structural pruning methods.
+<div align="center">
+  <img src="images/paper/compare_tab2.PNG" width="50%">
+</div>
+
+
+
+
+## <a name="Models"></a>Model Using
+
+Fast state_dict loading for local uniform pruning SlimSAM-50 model:
 
 ``` python
 model = SamModel.from_pretrained("Zigeng/SlimSAM-uniform-77").to("cuda")
@@ -15,4 +72,53 @@ inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to
 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!
+
+```bibtex
+@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
+
+<details>
+<summary>
+<a href="https://github.com/facebookresearch/segment-anything">SAM</a> (Segment Anything) [<b>bib</b>]
+</summary>
+
+```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}
+}
+```
+</details>
+
+
+
+<details>
+<summary>
+<a href="https://github.com/VainF/Torch-Pruning">Torch Pruning</a> (DepGraph: Towards Any Structural Pruning) [<b>bib</b>]
+</summary>
+
+```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}
+}
+```
+</details>