base on EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything ## Update -- Efficient Track Anything Our new release on [**Efficient Track Anything**](https://github.com/yformer/EfficientTAM). * Efficient Track Anything code: https://github.com/yformer/EfficientTAM * Efficient Track Anything project (with gradio demo): https://yformer.github.io/efficient-track-anything/ * Efficient Track Anything paper: https://arxiv.org/pdf/2411.18933  **Efficient Track Anything** is an efficient foundation model for promptable unified image and video segmentation. [`🤗Efficient Track Anything for video segmentation`](https://5c9036562e75ee2d4d.gradio.live) [`🤗Efficient Track Anything for image segment everything`](https://5239f8e221db7ee8a0.gradio.live) [`🤗Efficient Track Anything checkpoints`](https://huggingface.co/yunyangx/efficient-track-anything/tree/main) # EfficientSAM EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything ## News [Jan.12 2024] ONNX version of EfficientSAM including separate encoder and decoder is available on the [Hugging Face Space](https://huggingface.co/spaces/yunyangx/EfficientSAM/tree/main) (thanks to @wkentaro Kentaro Wada for implementing onnx export) [Dec.31 2023] EfficientSAM is integrated into the annotation tool, [Labelme](https://github.com/labelmeai/labelme) (huge thanks to lableme team and @wkentaro Kentaro Wada) [Dec.11 2023] The EfficientSAM model code with checkpoints is fully available in this repository. The [example](https://github.com/yformer/EfficientSAM/blob/main/EfficientSAM_example.py) script shows how to instantiate the model with checkpoint and query points on an image. [Dec.10 2023] Grounded EfficientSAM demo is available on [Grounded-Efficient-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything/tree/main/EfficientSAM) (huge thanks to IDEA-Research team and @rentainhe for supporting [grounded-efficient-sam demo](https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/main/EfficientSAM/grounded_efficient_sam.py) under [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything)). [Dec.6 2023] EfficientSAM demo is available on the [Hugging Face Space](https://huggingface.co/spaces/yunyangx/EfficientSAM) (huge thanks to all the HF team for their support). [Dec.5 2023] We release the torchscript version of EfficientSAM and share a colab. ## Online Demo & Examples Online demo and examples can be found in the [project page](https://yformer.github.io/efficient-sam/). ## EfficientSAM Instance Segmentation Examples | | | :-------------------------:|:-------------------------: Point-prompt |  Box-prompt |  Segment everything | Saliency |  ## Model EfficientSAM checkpoints are available under the weights folder of this github repository. Example instantiations and run of the models can be found in [EfficientSAM_example.py](https://github.com/yformer/EfficientSAM/blob/main/EfficientSAM_example.py). | EfficientSAM-S | EfficientSAM-Ti | |------------------------------|------------------------------| | [Download](https://github.com/yformer/EfficientSAM/blob/main/weights/efficient_sam_vits.pt.zip) |[Download](https://github.com/yformer/EfficientSAM/blob/main/weights/efficient_sam_vitt.pt)| You can directly use EfficientSAM with checkpoints, ``` from efficient_sam.build_efficient_sam import build_efficient_sam_vitt, build_efficient_sam_vits efficientsam = build_efficient_sam_vitt() ``` ## Jupyter Notebook Example The notebook is shared [here](https://github.com/yformer/EfficientSAM/blob/main/notebooks) ## 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} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"5739","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"