Image Segmentation
ONNX
BiRefNet
onnxruntime
background-removal
browser
clip-art
print-on-demand
t-shirt-design
knowledge-distillation
Instructions to use bowespublishing/crisp-cut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use bowespublishing/crisp-cut with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("bowespublishing/crisp-cut", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("bowespublishing/crisp-cut") - Notebooks
- Google Colab
- Kaggle
Download onnx/crispcut-quality.onnx from bowespublishing/crisp-cut: direct link, hf CLI and curl.
- Browser
- Download file 26.5 MB
-
https://huggingface.co/bowespublishing/crisp-cut/resolve/main/onnx/crispcut-quality.onnx
- Command line
-
hf download hf://bowespublishing/crisp-cut/onnx/crispcut-quality.onnx
-
curl -L -o crispcut-quality.onnx https://huggingface.co/bowespublishing/crisp-cut/resolve/main/onnx/crispcut-quality.onnx
26.5 MB
- Xet hash:
- 52aefd7d4ad1943373aeb92d745c584d67cceaf1bd08bb3a8245a927330e1864
- Size of remote file:
- 26.5 MB
- SHA256:
- 31a3274576151a40bcd1f81ebf778508ae5e4707fc9669b205b65ce9dde92968
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