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https://github.com/xinghaochen/tinysam

Official PyTorch implementation of "TinySAM: Pushing the Envelope for Efficient Segment Anything Model"
https://github.com/xinghaochen/tinysam

efficient-sam sam segment-anything tinysam

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Official PyTorch implementation of "TinySAM: Pushing the Envelope for Efficient Segment Anything Model"

Host: GitHub
URL: https://github.com/xinghaochen/tinysam
Owner: xinghaochen
License: apache-2.0
Created: 2023-12-19T11:25:54.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-10-05T11:04:30.000Z (3 months ago)
Last Synced: 2024-12-15T12:04:19.524Z (12 days ago)
Topics: efficient-sam, sam, segment-anything, tinysam
Language: Python
Homepage:
Size: 12.8 MB
Stars: 406
Watchers: 14
Forks: 23
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # TinySAM

### **TinySAM: Pushing the Envelope for Efficient Segment Anything Model**

*Han Shu, Wenshuo Li, Yehui Tang, Yiman Zhang, Yihao Chen, Houqiang Li, Yunhe Wang, Xinghao Chen*

*arXiv 2023*





[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/shuh15/TinySAM)

https://github.com/user-attachments/assets/4239f4c7-1e71-4be0-a59d-b81e6d881645

## Updates

* **2024/01/06**: [Demo](https://openxlab.org.cn/apps/detail/shuh15/TinySAM) of TinySAM is now available in **OpenXLab**. Thanks for the GPU grant.

* **2023/12/27**: [Models](https://huggingface.co/merve/tinysam) and [demo](https://huggingface.co/spaces/merve/tinysam) of TinySAM are now available in **Hugging Face**. Thanks for [merveenoyan](https://github.com/merveenoyan).

* **2023/12/27**: Pre-trained models and codes of [Q-TinySAM](#usage) (quantized variant) are released.

* **2023/12/27**: [Evaluation](#evaluation) codes for zero-shot instance segmentation task on COCO are released.

* **2023/12/22**: Pre-trained models and codes of TinySAM are released both in [Pytorch](https://github.com/xinghaochen/TinySAM) and [Mindspore](https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM).

## Overview

We propose a framework to obtain a tiny segment anything model (**TinySAM**) while maintaining the strong zero-shot performance. We first propose a full-stage knowledge distillation method with online hard prompt sampling strategy to distill a lightweight student model. We also adapt the post-training quantization to the promptable segmentation task and further reducing the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by with almost no performance degradation. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task. Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods.

![framework](./fig/framework.png)



^{Figure 1: Overall framework and zero-shot results of TinySAM.}



![everything](./fig/everything.png)



^{Figure 2: Our hierarchical strategy for everything mode.}



![vis](./fig/vis.png)



^{Figure 3: Visualization results of TinySAM.}



## Requirements

The code requires `python>=3.7` and we use `torch==1.10.2` and `torchvision==0.11.3`. To visualize the results, `matplotlib>=3.5.1` is also required.  

- python 3.7

- pytorch == 1.10.2

- torchvision == 0.11.3

- matplotlib==3.5.1

## Usage

1. Download [checkpoints](#evaluation) into the directory of *weights*.

2. Run the demo code for single prompt of point or box.

```

python demo.py

```

3. Run the demo code for hierarchical segment everything strategy.

```

python demo_hierachical_everything.py

```

4. Run the demo code for quantization inference.

```

python demo_quant.py

```

## Evaluation

We follow the setting of original [SAM](https://arxiv.org/abs/2304.02643) paper and evaluate the zero-shot instance segmentaion on COCO and LVIS dataset. The experiment results are described as followed.

| Model               | FLOPs (G) |COCO AP (%) | LVIS AP (%)| 

| ------------------- | -------- | ------- |------- |

| SAM-H                 |2976| 46.6/46.5*     | 44.7       | 

| SAM-L                 |1491| 46.2/45.5*     | 43.5       | 

| SAM-B                 |487| 43.4/41.0*     | 40.8       | 

| FastSAM                 |344| 37.9     | 34.5       | 

| MobileSAM            | 42.0|41.0     | 37.0       | 

| **TinySAM**  [\[ckpt\]](https://github.com/xinghaochen/TinySAM/releases/download/1.0/tinysam.pth)       | 42.0|41.9     | 38.6       | 

| **Q-TinySAM**  [\[ckpt\]](https://github.com/xinghaochen/TinySAM/releases/download/2.0/tinysam_w8a8.pth)            | 20.3|41.3     | 37.2      | 

^{\* Results of single output ([`multimask_output=False`](https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/predictor.py#L98)).}

First download the detection boxes ([`coco_instances_results_vitdet.json`](https://github.com/xinghaochen/TinySAM/releases/download/2.0/coco_instances_results_vitdet.json)) produced by ViTDet model, as well as the ground-truth instance segmentation labels([`instances_val2017.json`](https://github.com/xinghaochen/TinySAM/releases/download/2.0/instances_val2017.json)) and put them into `eval/json_files`. 

Related json files for LVIS dataset are available in [`lvis_instances_results_vitdet.json`](https://github.com/xinghaochen/TinySAM/releases/download/3.0/lvis_instances_results_vitdet.json) and [`lvis_v1_val.json`](https://github.com/xinghaochen/TinySAM/releases/download/3.0/lvis_v1_val.json).

Run the following code to perform evaluation for zero-shot instance segmentation on COCO dataset.

```

cd eval; sh eval_coco.sh

```

The results should be:

```

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.419

Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.683

Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.436

Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.260

Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.456

Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.583

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.325

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.511

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.532

Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.390

Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.577

Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.671

```

## Acknowledgements

We thank the following projects: [SAM](https://github.com/facebookresearch/segment-anything), [MobileSAM](https://github.com/ChaoningZhang/MobileSAM), [TinyViT](https://github.com/microsoft/Cream).

## Citation

```bibtex

@article{tinysam,

  title={TinySAM: Pushing the Envelope for Efficient Segment Anything Model},

  author={Shu, Han and Li, Wenshuo and Tang, Yehui and Zhang, Yiman and Chen, Yihao and Li, Houqiang and Wang, Yunhe and Chen, Xinghao},

  journal={arXiv preprint arXiv:2312.13789},

  year={2023}

}

```

## License

This project is licensed under  Apache License 2.0. Redistribution and use should follow this license.