Image Segmentation
Transformers
Safetensors
English
layer decomposition
image segmentation
image matting
design
custom_code
Instructions to use cyberagent/layerd-birefnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cyberagent/layerd-birefnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="cyberagent/layerd-birefnet", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("cyberagent/layerd-birefnet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Tomoyuki Suzuki commited on
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## LayerD BiRefNet Matting Module
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This repository contains the code and model weights for the matting module in [LayerD [ICCV'25]](https://arxiv.org/abs/), a layer decomposition framework for graphic design images.
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The model in this repository is intended to be used as a part of the original LayerD github repository.
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Please visit
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The model architecture code is based on the [BiRefNet repository](https://huggingface.co/ZhengPeng7/BiRefNet), and we thank the authors for releasing their high-quality matting model and code.
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## LayerD BiRefNet Matting Module
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This repository contains the code and model weights for the matting module in [LayerD [ICCV'25]](https://arxiv.org/abs/), a layer decomposition framework for graphic design images.
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The model in this repository is intended to be used as a part of the original [LayerD github repository](https://github.com/CyberAgentAILab/LayerD).
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Please visit https://github.com/CyberAgentAILab/LayerD for more information.
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The model architecture code is based on the [BiRefNet repository](https://huggingface.co/ZhengPeng7/BiRefNet), and we thank the authors for releasing their high-quality matting model and code.
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