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README.md

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+---
+license: mit
+library_name: onnx
+tags:
+  - onnx
+  - image-segmentation
+  - background-removal
+  - birefnet
+pipeline_tag: image-segmentation
+---
+
+# BiRefNet_lite — browser-tuned ONNX export (static 1024², opset 17)
+
+Custom ONNX export of **BiRefNet_lite** (bilateral reference network for
+dichotomous image segmentation, Swin-v1-tiny backbone) from the official
+[ZhengPeng7/BiRefNet_lite](https://huggingface.co/ZhengPeng7/BiRefNet_lite)
+weights, re-hosted for [AllPrivate](https://allprivate.app) — where every model
+runs in the visitor's browser and nothing is uploaded.
+
+## Why a custom export
+
+The community export ([onnx-community/BiRefNet_lite-ONNX](https://huggingface.co/onnx-community/BiRefNet_lite-ONNX))
+throws `OrtRun std::bad_alloc` in ONNX Runtime Web on every EP (fp32/fp16 ×
+wasm/webgpu, tested 2026-07 on an M-series MacBook): it is a dynamic-shape
+trace whose deformable convolutions decompose into GatherND/ScatterND/Clip
+chains that fall back to CPU on the WebGPU EP and materialize im2col tensors of
+hundreds of MB inside the 32-bit wasm heap.
+
+This export replaces `DeformableConv2d.forward` with a numerically identical
+per-kernel-tap **GridSample** decomposition (one bilinear GridSample + 1×1 Conv
+per tap, accumulated — peak extra memory is one `[1,C,H,W]` tensor per tap) and
+traces with a **static** input shape, so all shape dynamism constant-folds
+away. It runs to completion on both the wasm and WebGPU execution providers of
+ONNX Runtime Web (verified 1.26-dev).
+
+## Provenance
+
+- Source weights: [`model.safetensors`](https://huggingface.co/ZhengPeng7/BiRefNet_lite/blob/main/model.safetensors)
+  at pinned revision [`7838f1c`](https://huggingface.co/ZhengPeng7/BiRefNet_lite/tree/7838f1c3472f827cd8ce13ab5ccc2ce48077360f)
+  - sha256: `4417d89795250e698c3cb0ae8df15743810065f646f48a694fdfa7ca052d0815`
+- Exported with `torch.onnx.export` (PyTorch 2.8.0, TorchScript exporter),
+  opset 17, fp32, constant folding on, post-processed with onnxslim 0.1.94
+  (constant-folds the Swin attention-mask construction; Gemm fusion disabled)
+  plus a content-hash initializer dedupe (the backbone is traced twice for the
+  multi-scale 'cat' input) — see [`export_birefnet_lite.py`](./export_birefnet_lite.py)
+  for the exact reproducible script
+- I/O: `input_image` `[1, 3, 1024, 1024]` (NCHW, RGB, float32, ImageNet
+  normalization: `(x/255 - mean) / std`, mean `[0.485, 0.456, 0.406]`, std
+  `[0.229, 0.224, 0.225]`) → `output_image` `[1, 1, 1024, 1024]` **logits**;
+  apply sigmoid for the [0,1] matte
+- Graph ops: `Add, BatchNormalization, Concat, Conv, Div, Erf, Gather,
+  GlobalAveragePool, GridSample, LayerNormalization, MatMul, Mul, Pad, Relu,
+  Reshape, Resize, Shape, Sigmoid, Slice, Softmax, Transpose, Unsqueeze` —
+  no GatherND, no ScatterND, no Clip
+- Validated against the unpatched PyTorch reference (torchvision
+  `deform_conv2d`): GridSample decomposition max abs dev `3.1e-5` vs
+  torchvision; full-model ONNX (onnxruntime CPU) max abs logits dev `1.0e-4`,
+  max post-sigmoid dev `1.3e-9`
+- File sha256: `50a57872cc739192446da2a934159f957c81af8b5a161dfda8e3daa51660ca67`
+
+## License
+
+MIT, inherited from [BiRefNet](https://github.com/ZhengPeng7/BiRefNet/blob/main/LICENSE)
+(© Zheng Peng et al.). This repo only re-packages the officially released
+weights in ONNX form.
+
+## Citation
+
+```bibtex
+@article{BiRefNet,
+  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}
+}
+```