BiRefNet-lite (ONNX, native DeformConv)
BiRefNet-lite by Zheng Peng et al., converted to ONNX for AssetFurnace, which uses it to cut the subject out of a picture before turning it into a 3D model.
The weights are unchanged. The difference is the export: this file uses ONNX's built-in DeformConv
layer (opset 19) instead of breaking each deformable convolution into many small steps. On an Apple
Silicon Mac with ONNX Runtime 1.30 (CPU), one 1024 x 1024 cut-out:
| File | Peak memory | Time |
|---|---|---|
| Common BiRefNet-lite ONNX export | 9.5 GB | ~5 s |
| This file | 4.0 GB | 1.8 s |
Same output: on our test pictures the two masks agree to 99.8% or better.
- Input
input_image: float32 [1, 3, 1024, 1024], ImageNet-normalised RGB. - Output
output_image: [1, 1, 1024, 1024] raw scores; apply a sigmoid to get the mask (the same contract as rembg'sbirefnet-general-lite, so it is a drop-in). - Needs ONNX Runtime 1.20 or newer (native DeformConv).
- sha256
a97176abb6fe55525b11113bf5bead5a9b5866b2c0d4ac058186cabb86cadfff, 180666557 bytes.
Converted from ZhengPeng7/BiRefNet_lite revision aa62cd87eafb9cc43056d08ef3615a14628b831d.
MIT licence, as the original (see LICENSE). All credit for the model to the BiRefNet authors.