| --- |
| license: apache-2.0 |
| library_name: onnx |
| pipeline_tag: image-segmentation |
| tags: |
| - onnx |
| - onnxruntime-web |
| - background-removal |
| - salient-object-detection |
| - u2net |
| --- |
| |
| # u2netp (ONNX) |
|
|
| A mirror of **u2netp** — the small ("portable") variant of |
| [U²-Net](https://github.com/xuebinqin/U-2-Net) — for salient object detection, |
| the model behind background removal. |
|
|
| This repository exists so the model can be fetched directly by a browser at |
| runtime. It is a **verbatim mirror**: the file is byte-for-byte the `u2netp.onnx` |
| asset published by the [rembg](https://github.com/danielgatis/rembg) project, and |
| its SHA-256 is pinned by the consumer. |
|
|
| | | | |
| | --- | --- | |
| | File | `u2netp.onnx` | |
| | Size | 4,574,861 bytes (4.36 MiB) | |
| | SHA-256 | `309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8` | |
| | Opset | 11 | |
|
|
| ## Tensor contract |
|
|
| Read the input and output names off the graph rather than hardcoding them — the |
| first of each is what the pipeline below uses. |
|
|
| - **Input** — `float32[1, 3, 320, 320]`, NCHW, RGB. |
| - **Output** — `float32[1, 1, 320, 320]`, a saliency map. U²-Net emits several |
| side outputs; the **first** is the fused one to use. |
|
|
| ## Pre / post-processing |
|
|
| The model is fixed at 320×320, so the image is **squashed** to that size (aspect |
| ratio is not preserved), then normalised with the ImageNet statistics: |
|
|
| ``` |
| scaled = (pixel / 255) / max_pixel_value_of_the_image |
| tensor = (scaled - mean) / std |
| mean = [0.485, 0.456, 0.406] |
| std = [0.229, 0.224, 0.225] |
| ``` |
|
|
| Afterwards, min–max normalise the saliency map to 0–1, scale to 0–255, resize it |
| back to the original dimensions (bilinear), and use it as the alpha channel of |
| the source image. |
|
|
| > **Note on other mirrors.** Some copies of this model ship a |
| > `preprocessor_config.json` describing a letterbox resize |
| > (`keep_aspect_ratio` / `do_pad`). That does **not** match the pipeline above, |
| > and following it changes the output. |
|
|
| ## Licence and credit |
|
|
| The U²-Net weights are **Apache-2.0**. If you use them, cite the paper: |
|
|
| ```bibtex |
| @InProceedings{Qin_2020_PR, |
| title = {U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection}, |
| author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Dehghan, Masood |
| and Zaiane, Osmar and Jagersand, Martin}, |
| journal = {Pattern Recognition}, |
| volume = {106}, |
| pages = {107404}, |
| year = {2020} |
| } |
| ``` |
|
|
| - Model and paper: [xuebinqin/U-2-Net](https://github.com/xuebinqin/U-2-Net) (Apache-2.0) |
| - The release asset mirrored here: [danielgatis/rembg](https://github.com/danielgatis/rembg) (MIT) |
|
|
