Model Details
Full Model IDonnx-community/BiRefNet_lite-ONNX
Pipeline / Taskimage-segmentation
Librarytransformers.js
Downloads (all-time)8.3K
Likes17
Last Modified10/26/2025
Author / Orgonnx-community
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("image-segmentation", model="onnx-community/BiRefNet_lite-ONNX")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformers.jsonnxswinbackground-removalmask-generationDichotomous Image SegmentationCamouflaged Object DetectionSalient Object Detectionimage-segmentationbase_model:ZhengPeng7/BiRefNet_litebase_model:quantized:ZhengPeng7/BiRefNet_litelicense:mitregion:us
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: image-segmentation
This model is designed for the image-segmentation task. Explore more models for this use case.
All image-segmentation Models →📊 Popularity
⬇ Downloads8.3K
❤️ Community Likes17
🛠️ Requirements
- →Install: pip install transformers.js
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.