https://github.com/xetdata/efficientsam
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
https://github.com/xetdata/efficientsam
Last synced: about 1 year ago
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EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
- Host: GitHub
- URL: https://github.com/xetdata/efficientsam
- Owner: xetdata
- License: apache-2.0
- Created: 2023-12-06T17:52:15.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-06T18:31:50.000Z (over 2 years ago)
- Last Synced: 2025-03-24T22:51:20.023Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 18.6 KB
- Stars: 17
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# EfficientSAM
[XetHub](https://about.xethub.com) hosted fork of [EfficientSAM](https://github.com/yformer/EfficientSAM) using the [GitHub plugin](https://github.com/apps/xetdata).
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
## News
[Dec.5 2023] We release the torchscript version of EfficientSAM and share the Google colab notebook.
## Online Demo & Examples
Online demo and examples can be found in the [project page](https://yformer.github.io/efficient-sam/).
## EfficientSAM Instance Segmentation Examples
## Using the models
The model files live in this repo itself:
- EfficientSAM-S:[models/efficientsam_s_gpu.jit](models/efficientsam_s_gpu.jit)
- EfficientSAM-Ti: [models/efficientsam_ti_gpu.jit](models/efficientsam_ti_gpu.jit)
**Instructions**
1. Install the [git-xet](https://xethub.com/assets/docs/getting-started/install) extension.
2. Clone the repo:
```
# Using SSH
git clone git@github.com:xetdata/EfficientSAM.git
cd EfficientSAM/
```
2. Activate your Python virtual environment and install the requirements:
```
pip install -r requirements.txt
```
3. Start Jupyter Notebook:
```
# jupyter lab
jupyter lab
# jupyter notebook
jupyter notebook
```
4. You can directly use EfficientSAM:
```
import torch
efficientsam = torch.jit.load(models/efficientsam_s_gpu.jit)
```
## Google Colab
You can also use this [Google Colab notebook](https://colab.research.google.com/drive/150dvh_lwbliC3020fWO9qASgy-so6sUZ?usp=sharing)
## Acknowledgement
+ [SAM](https://github.com/facebookresearch/segment-anything)
+ [MobileSAM](https://github.com/ChaoningZhang/MobileSAM)
+ [FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM)
+ [U-2-Net](https://github.com/xuebinqin/U-2-Net)
If you're using EfficientSAM in your research or applications, please cite using this BibTeX:
```bibtex
@article{xiong2023efficientsam,
title={EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything},
author={Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra},
journal={arXiv:2312.00863},
year={2023}
}
```