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README.md

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+---
+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)