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license: cc-by-4.0 | |
title: ZIM demo with SAM | |
emoji: π | |
colorFrom: yellow | |
colorTo: pink | |
sdk: gradio | |
sdk_version: 4.38.1 | |
app_file: app.py | |
pinned: false | |
python_version: 3.10.12 | |
short_description: ZIM demo comparison with SAM | |
# ZIM: Zero-Shot Image Matting for Anything | |
## Introduction | |
π Introducing ZIM: Zero-Shot Image Matting β A Step Beyond SAM! π | |
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. | |
π What is ZIM? π | |
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. | |
π Get Started with ZIM π | |
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} | |
} |