Add model card

README.md

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+---
+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/` | 467 MB | Half precision. Best choice on a GPU (recommended download) |
+| `birefnet-int8/` | 295 MB | 8-bit. Closest to full precision of the small ones |
+| `birefnet-int4/` | 199 MB | 4-bit backbone, 8-bit elsewhere. Smallest |
+| `birefnet-fp32/` | 928 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/`.