u2netp (ONNX)

A mirror of u2netp — the small ("portable") variant of U²-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 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:

@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 (Apache-2.0)
• The release asset mirrored here: danielgatis/rembg (MIT)