skillsafe-ai/u2netp

U²-Net-p small background removal

Browser-ready import artifacts for background-removal, produced by SkillSafe's reproducible converter (models/ in skillsafe.ai) from a pinned upstream source. Every byte here is derivable from that source plus the recipe below; nothing was edited by hand.

Provenance

Upstream	https://huggingface.co/tomjackson2023/rembg/tree/cd3a3d6767a7859efea31ef0f2f373582cf06d82
Upstream SHA-256 / commit	cd3a3d6767a7859efea31ef0f2f373582cf06d82
Recipe	recipes/u2netp.yaml — sha256 207ffd0ebb549914179faa39602301d0c607504cd4270ce035acfd339e26532b
Toolchain	Python 3.12.13, torch 2.10.0, onnx 1.23.0, onnxruntime 1.30.0 on Darwin 25.6.0 arm64
Converted	2026-09-22T21:39:24+00:00

Files

file	class	size	SHA-256
u2netp.onnx	registry	4.36 MB	309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8

registry files are parameter files served from models.skillsafe.ai once vetted; bundle files ship inside an app; registry-shared is a runtime library reused by every model of the same architecture.

Verification

Imported as published upstream (no conversion). Each file is pinned by SHA-256 to its source; every ONNX file passed onnx.checker and a CPU smoke run under onnxruntime with zero-filled inputs at the declared shapes:

file	inputs	outputs	ms
u2netp.onnx	input.1[1, 3, 320, 320]	1959[1, 1, 320, 320], 1960[1, 1, 320, 320], 1961[1, 1, 320, 320], 1962[1, 1, 320, 320], 1963[1, 1, 320, 320], 1964[1, 1, 320, 320], 1965[1, 1, 320, 320]	104.1

Use in the browser

import * as ort from "onnxruntime-web";
const session = await ort.InferenceSession.create("https://huggingface.co/skillsafe-ai/u2netp/resolve/main/u2netp.onnx", { executionProviders: ["webgpu", "wasm"] });

Contract (u2netp.onnx): input input.1 float32 [1, 3, 320, 320] → output 1959 float32 [1, 1, 320, 320], 1960 float32 [1, 1, 320, 320], 1961 float32 ['Sigmoid1961_dim_0', 'Sigmoid1961_dim_1', 'Sigmoid1961_dim_2', 'Sigmoid1961_dim_3'], 1962 float32 ['Sigmoid1962_dim_0', 'Sigmoid1962_dim_1', 'Sigmoid1962_dim_2', 'Sigmoid1962_dim_3'], 1963 float32 ['Sigmoid1963_dim_0', 'Sigmoid1963_dim_1', 'Sigmoid1963_dim_2', 'Sigmoid1963_dim_3'], 1964 float32 ['Sigmoid1964_dim_0', 'Sigmoid1964_dim_1', 'Sigmoid1964_dim_2', 'Sigmoid1964_dim_3'], 1965 float32 ['Sigmoid1965_dim_0', 'Sigmoid1965_dim_1', 'Sigmoid1965_dim_2', 'Sigmoid1965_dim_3']. Opset 11.

Licence and attribution

U²-Net-p: Xuebin Qin et al., Apache License 2.0 (https://github.com/xuebinqin/U-2-Net); ONNX as distributed by rembg (MIT).

Licence: Apache-2.0 — notice: https://github.com/xuebinqin/U-2-Net/blob/master/LICENSE. The conversion recipe and this model card are part of the SkillSafe repository and carry its licence; the weights remain under the upstream licence above.

The full manifest.json in this repo records the recipe, sources, toolchain (including the uv.lock hash) and per-file verification numbers.