---
title: efficient-sam-ti
canonical_url: "https://www.modelscope.cn/models/tany0699/efficient-sam-ti"
md_url: "https://www.modelscope.cn/models/tany0699/efficient-sam-ti.md"
repository: tany0699/efficient-sam-ti
chinese_name: "轻量级SAM模型Ti"
last_updated: 2023-12-11
license: "Apache License 2.0"
pipeline_tag: image-segmentation
tasks:
  - image-segmentation
model_type:
  - Hybrid
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - zh
domain:
  - cv
downloads: 56
stars: 3
tags:
  - segmentation
  - SAM
  - EfficientSAM
  - EfficientSAM-Ti
  - "高效"
  - "轻量化"
  - "分割一切"
  - "大模型"
  - "预训练"
---

# efficient-sam-ti

> efficient-sam-ti - tany0699 在 ModelScope 开源的模型。EfficientSAM &nbsp Paper &nbsp｜ &nbsp GitHub &nbsp

tany0699/efficient-sam-ti 是 ModelScope 魔搭社区上的image-segmentation模型，采用 Apache License 2.0 许可。

- **Repository**: tany0699/efficient-sam-ti
- **License**: Apache License 2.0
- **Tasks**: image-segmentation
- **Tags**: segmentation, SAM, EfficientSAM, EfficientSAM-Ti, 高效, 轻量化, 分割一切, 大模型, 预训练
- **Downloads**: 56
- **Stars**: 3
- **Last updated**: 2023-12-11

Source: https://www.modelscope.cn/models/tany0699/efficient-sam-ti

---

# EfficientSAM
<p align="center">
         &nbsp<a href="https://arxiv.org/pdf/2312.00863.pdf">Paper</a>&nbsp｜
         &nbsp<a href="https://github.com/yformer/EfficientSAM">GitHub </a>&nbsp
</p>

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything.

<p align="center">
<img src="./efficientSAM.jpg" width="640" height="488" alt="Comparative analysis of FastSAM and SAM"/>
</p>


## 期望模型使用方式以及适用范围
本模型适用范围较广，能对图片中包含的大部分感兴趣物体（COCO things 80类）根据提示(点、框、文本)进行分割。

### 如何使用
在ModelScope框架上，提供输入图片，即可通过简单的Pipeline调用来使用。

#### 代码范例
即将上线

#### Clone with HTTP
```bash
 git clone https://www.modelscope.cn/tany0699/efficient-sam-ti.git
```

## 引用
如果该模型对你有所帮助，请引用相关的论文：
```
 @article{Xiong_Varadarajan_Wu_Xiang_Xiao_Zhu_Dai_Wang_Sun_Iandola_et al._2023,  
 title={EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything}, 
 author={Xiong, Yunyang and Varadarajan, Bala and Wu, Lemeng and Xiang, Xiaoyu and Xiao, Fanyi and Zhu, Chenchen and Dai, Xiaoliang and Wang, Dilin and Sun, Fei and Iandola, Forrest and Krishnamoorthi, Raghuraman and Chandra, Vikas}, 
 year={2023}, 
 month={Dec}, 
 language={en-US} 
 }
```