---
license: mit
base_model: ZhengPeng7/BiRefNet_lite
tags:
  - background-removal
  - image-segmentation
  - dichotomous-image-segmentation
  - onnx
  - coreml
pipeline_tag: image-segmentation
---

# BiRefNet_lite — ONNX re-export for the CoreML execution provider

This is **not** a new model. It is [BiRefNet_lite](https://huggingface.co/ZhengPeng7/BiRefNet_lite)
by **Peng Zheng et al.** ([BiRefNet](https://github.com/ZhengPeng7/BiRefNet), MIT), re-exported to
ONNX so that ONNX Runtime's **CoreML execution provider** (Apple Silicon GPU) runs the whole graph
in a single partition. All credit for the model goes to the BiRefNet authors.

## Why a re-export

The published ONNX exports don't work well with the CoreML EP: the deformable convolution exports
as huge `GatherND` intermediates (>4 GB on CPU), and CoreML either fails to compile it or splits the
graph into ~100 partitions. This export (scripts in `scripts/`) changes only how operations are
expressed, not the weights:

1. `deform_conv2d` → one bilinear `GridSample` per kernel tap, accumulated with 1×1 convolutions
   (numerically equivalent, max logit difference ~5e-5).
2. Swin window partition/reverse and `image2patches` reshaped to rank ≤ 5 (CoreML's limit).
3. `qkv[0..2]` indexing → `unbind`.
4. Shapes fixed to 1024×1024, ORT basic constant folding, and Gemm weights rewritten to
   `transB=1` (`fix_gemm.py`) so the CoreML compile stays small and fast.

Result: 6924/6924 nodes on CoreML, ~0.4 s per image on an M3 Pro (CPU+GPU, MLProgram), output
matching the CPU EP to within 2e-4 in alpha.

## Usage

- File: `model.onnx` (fp32)
- Input `input_image`: `[1, 3, 1024, 1024]` float32 — RGB resized to 1024×1024 (bilinear, no crop),
  `(v/255 − mean) / std` with mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]`.
- Output `output_image`: `[1, 1, 1024, 1024]` **logits** — apply a sigmoid, resize to the image size.
- CoreML EP: MLProgram format, static shapes, compute units CPU+GPU.

Used by Mixer, a personal D&D session app, for in-app background removal of character art.

## License

MIT, same as the original BiRefNet. Please cite the original work:

```
@article{zheng2024birefnet,
  title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
  author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
  journal={CAAI Artificial Intelligence Research},
  year={2024}
}
```