| --- |
| library_name: onnx |
| license: mit |
| pipeline_tag: image-segmentation |
| tags: |
| - onnx |
| - birefnet |
| - background-removal |
| - image-segmentation |
| base_model: |
| - ZhengPeng7/BiRefNet |
| --- |
| |
| # imageback models |
|
|
| ONNX builds of [BiRefNet](https://github.com/ZhengPeng7/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](https://huggingface.co/onnx-community/BiRefNet-ONNX) and stays under |
| the original MIT licence. |
|
|
| | Folder | Size | Weights | |
| | --- | --- | --- | |
| | `birefnet-fp16/` | 490 MB | Half precision. Best choice on a GPU (recommended download) | |
| | `birefnet-int8/` | 309 MB | 8-bit. Closest to full precision of the small ones | |
| | `birefnet-int4/` | 209 MB | 4-bit backbone, 8-bit elsewhere. Smallest | |
| | `birefnet-fp32/` | 973 MB | Full precision; the source for quantizing | |
|
|
| Each folder holds `model.onnx` and `config.json` (input size and normalisation). |
|
|
| ```sh |
| 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: |
|
|
| | Model | Mean absolute difference | Pixels differing by more than 0.1 | |
| | --- | --- | --- | |
| | fp16 | 0.00017 | 0.001% | |
| | int8 | 0.00011 | 0.000% | |
| | int4 | 0.00081 | 0.076% | |
|
|
| The scripts that produced these files are in the imageback repository under `tools/export/`. |
|
|
