Update README.md

README.md

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+license: cc-by-4.0
 title: ZIM demo with SAM
 emoji: 📈
 colorFrom: yellow
@@ -11,4 +12,65 @@ python_version: 3.10.12
 short_description: ZIM demo comparison with SAM
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ZIM: Zero-Shot Image Matting for Anything
+
+## Introduction
+
+🚀 Introducing ZIM: Zero-Shot Image Matting – A Step Beyond SAM! 🚀
+
+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.
+
+🔍 Get Started with ZIM 🔍
+
+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(<image>)
+masks, _, _ = predictor.predict(<input_prompts>)
+```
+
+## 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}
+}