imageback models

ONNX builds of BiRefNet used by imageback, a C++ command-line tool that replaces the background of a photo with an image, a colour or transparency. They run on ONNX Runtime (CPU, CUDA or WebGPU) with no Python involved.

Nothing here was trained or fine-tuned by us. Each folder is derived from the ONNX export at onnx-community/BiRefNet-ONNX and stays under the original MIT licence.

Each folder holds model.onnx and config.json (input size and normalisation).

hf download baker76/imageback-models --include "birefnet-fp16/*" --local-dir models
imageback replace photo.jpg -o cutout.png --model models/birefnet-fp16

Using the models elsewhere

Input input_image: float32 [1, 3, 1024, 1024], RGB scaled to 0-1 and normalised with the ImageNet mean and standard deviation; the picture is stretched to 1024 x 1024. Output output_image: float32 [1, 1, 1024, 1024] logits; apply a sigmoid and resize to the picture's size to get the alpha matte.

Changes from the upstream export

• All folders: Split nodes with 16 or 32 outputs are replaced by two levels of smaller ones. WebGPU allows a shader 16 buffers, which the original nodes exceed. Results are unchanged.
• birefnet-int8, birefnet-int4: made from the fp32 graph. The Swin backbone's MatMul weights (84% of the model) are stored as 8- or 4-bit blocks of 32 (ONNX Runtime MatMulNBits). Convolution kernels and relative position biases are stored as 8-bit integers behind DequantizeLinear. Activations stay float32, so these are smaller than fp16 but not faster.

Accuracy

Difference between each model's alpha matte and the fp32 model's, on five test photos (people, a dog, a cat, a bicycle), with the CUDA provider:

The scripts that produced these files are in the imageback repository under tools/export/.