Instructions to use moonbox-dev/BiRefNet_lite-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use moonbox-dev/BiRefNet_lite-ONNX with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("moonbox-dev/BiRefNet_lite-ONNX", 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("moonbox-dev/BiRefNet_lite-ONNX") - Notebooks
- Google Colab
- Kaggle
Download int4/model.onnx from moonbox-dev/BiRefNet_lite-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 81.7 MB
-
https://huggingface.co/moonbox-dev/BiRefNet_lite-ONNX/resolve/main/int4/model.onnx
- Command line
-
hf download hf://moonbox-dev/BiRefNet_lite-ONNX/int4/model.onnx
-
curl -L -o model.onnx https://huggingface.co/moonbox-dev/BiRefNet_lite-ONNX/resolve/main/int4/model.onnx
81.7 MB
- Xet hash:
- 2834097f4791b24bc618ac71b00dc055d57940440655ebc9c421f1ce28f845b6
- Size of remote file:
- 81.7 MB
- SHA256:
- f1569d7629559f3a4045cdc7189d1b6f0f170b070ff566596075c339e4178101
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