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README.md
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---
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license: cc-by-4.0
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---
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license: cc-by-4.0
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language:
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- en
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pipeline_tag: image-segmentation
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library_name: zim-anything
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tags:
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- matting
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- segmentation
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- segment anything
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- zero-shot matting
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---
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# ZIM-Anything-ViTL
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## Introduction
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π Introducing ZIM: Zero-Shot Image Matting β A Step Beyond SAM! π
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While SAM (Segment Anything Model) has redefined zero-shot segmentation with broad applications across multiple fields, it often falls short in delivering high-precision, fine-grained masks. Thatβs where ZIM comes in.
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π What is ZIM? π
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ZIM (Zero-Shot Image Matting) is a groundbreaking model developed to set a new standard in precision matting while maintaining strong zero-shot capabilities. Like SAM, ZIM can generalize across diverse datasets and objects in a zero-shot paradigm. But ZIM goes beyond, delivering highly accurate, fine-grained masks that capture intricate details.
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π Get Started with ZIM π
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Ready to elevate your AI projects with unmatched matting quality? Access ZIM on our project page: https://naver-ai.github.io/ZIM/
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## Installation
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```bash
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pip install zim_anything
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```
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or
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```bash
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git clone https://github.com/naver-ai/ZIM.git
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cd ZIM; pip install -e .
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```
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## Usage
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1. Make the directory `zim_vit_l_2092`.
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2. Download the [encoder](https://huggingface.co/naver-iv/zim-anything-vitl/resolve/main/zim_vit_l_2092/encoder.onnx?download=true) weight and [decoder](https://huggingface.co/naver-iv/zim-anything-vitl/resolve/main/zim_vit_l_2092/decoder.onnx?download=true) weight.
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3. Put them under the `zim_vit_b_2092` directory.
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```python
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from zim_anything import zim_model_registry, ZimPredictor
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backbone = "vit_l"
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ckpt_p = "zim_vit_l_2092"
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model = zim_model_registry[backbone](checkpoint=ckpt_p)
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if torch.cuda.is_available():
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model.cuda()
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predictor = ZimPredictor(model)
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predictor.set_image(<image>)
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masks, _, _ = predictor.predict(<input_prompts>)
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```
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## Citation
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If you find this project useful, please consider citing:
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```bibtex
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@article{kim2024zim,
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title={ZIM: Zero-Shot Image Matting for Anything},
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author={Kim, Beomyoung and Shin, Chanyong and Jeong, Joonhyun and Jung, Hyungsik and Lee, Se-Yun and Chun, Sewhan and Hwang, Dong-Hyun and Yu, Joonsang},
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journal={arXiv preprint arXiv:2411.00626},
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year={2024}
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}
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