--- license: cc-by-4.0 language: - en tags: - matting - segmentation - segment anything - zero-shot matting --- # Zero-Shot Image Matting for Anything ## Introduction πŸš€ Introducing ZIM: Zero-Shot Image Matting – A Step Beyond SAM! πŸš€ 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. That’s where ZIM comes in. 🌟 What is ZIM? 🌟 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. πŸ” Get Started with ZIM πŸ” Ready to elevate your AI projects with unmatched matting quality? Access ZIM on our [project page](https://naver-ai.github.io/ZIM/), [Arxiv](https://huggingface.co/papers/2411.00626), and [Github](https://github.com/naver-ai/ZIM). ## Installation ```bash pip install zim_anything ``` or ```bash git clone https://github.com/naver-ai/ZIM.git cd ZIM; pip install -e . ``` ## Usage 1. Make the directory `zim_vit_l_2092`. 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. 3. Put them under the `zim_vit_b_2092` directory. ```python from zim_anything import zim_model_registry, ZimPredictor backbone = "vit_l" ckpt_p = "zim_vit_l_2092" model = zim_model_registry[backbone](checkpoint=ckpt_p) if torch.cuda.is_available(): model.cuda() predictor = ZimPredictor(model) predictor.set_image() masks, _, _ = predictor.predict() ``` ## Citation If you find this project useful, please consider citing: ```bibtex @article{kim2024zim, title={ZIM: Zero-Shot Image Matting for Anything}, 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}, journal={arXiv preprint arXiv:2411.00626}, year={2024} }