File size: 2,042 Bytes
0a29d58 c9edb8a 0a29d58 621cd9b 0a29d58 77e95a5 4900c61 0a29d58 c1fd67f 0a29d58 4900c61 0a29d58 4900c61 0a29d58 ba63323 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | ---
library_name: nobg
tags:
- nobg-birefnet
- model_hub_mixin
- nobg
- pytorch_model_hub_mixin
license: apache-2.0
---
<p align="center">
<img src="https://usefeyn.com/feyn/feyn_mark.svg"/>
</p>
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
Library: [nobg](https://github.com/feyninc/nobg)
Blog : [feynobg blogpost](https://usefeyn.com/blog/feynobg/) [HF blogpost](https://huggingface.co/blog/feyninc/feynobg)
## how to use the model
```
pip install nobg>=0.2.5
```
```python
import torch
from loadimg import load_img
from nobg import AutoModel, AutoProcessor
model = AutoModel.from_pretrained("feyninc/FeyNobg").eval()
processor = AutoProcessor.from_pretrained("feyninc/FeyNobg")
image = load_img("input.jpg").convert("RGB")
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(pixel_values=inputs["pixel_values"])
alpha = processor.post_process_alpha_matting(
outputs, target_sizes=[(image.height, image.width)]
)[0]
processor.cutout(image, alpha).save("output.png")
```
## Citation
If you use this model, please cite:
```bibtex
@article{zheng2024birefnet,
title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
journal={CAAI Artificial Intelligence Research},
volume={3},
pages={9150038},
year={2024},
url={https://arxiv.org/abs/2401.03407},
}
```
## Contributions
Any contributions are welcome at https://github.com/feyninc/nobg |