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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...
Modellquelle
Quellenbeschreibung
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.
Quellen
1 QuelleVerifiziert 17. Okt.
Modellartefakte
1 ArtefaktQuellenauszüge
2 Auszüge| Upstream | https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx |
| Upstream SHA-256 / commit | v0.0.0 |
| Recipe | recipes/isnet-general-use.yaml — sha256 344b7fc0135d28c517f01921c74784fee61d4a31601d80b47d78ad582ada0781 |
| Toolchain | Python 3.12.13, torch 2.10.0, onnx 1.23.0, onnxruntime 1.30.0 on Darwin 25.6.0 arm64 |
| Converted | 2026-09-22T18:56:15+00:00 |
| file | class | size | SHA-256 |
|---|---|---|---|
isnet-general-use.onnx | registry | 170.37 MB | 60920e99c45464f2ba57bee2ad08c919a52bbf852739e96947fbb4358c0d964a |
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.
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 |
|---|---|---|---|
isnet-general-use.onnx | input_image[1, 3, 1024, 1024] | output_image[1, 1, 1024, 1024], 1890[1, 1, 1024, 1024], 1891[1, 1, 1024, 1024], 1892[1, 1, 1024, 1024], 1893[1, 1, 1024, 1024], 1894[1, 1, 1024, 1024], input.1252[1, 64, 512, 512], input.1088[1, 64, 256, 256], input.948[1, 128, 128, 128], input.832[1, 256, 64, 64], input.740[1, 512, 32, 32], input.656[1, 512, 16, 16] | 1480.6 |
import * as ort from "onnxruntime-web";
const session = await ort.InferenceSession.create("https://huggingface.co/skillsafe-ai/isnet-general-use/resolve/main/isnet-general-use.onnx", { executionProviders: ["webgpu", "wasm"] });
Contract (isnet-general-use.onnx): input input_image float32 [1, 3, 1024, 1024] → output output_image float32 [1, 1, 1024, 1024], 1890 float32 [1, 1, 1024, 1024], 1891 float32 [1, 1, 1024, 1024], 1892 float32 [1, 1, 1024, 1024], 1893 float32 [1, 1, 1024, 1024], 1894 float32 [1, 1, 1024, 1024], input.1252 float32 [1, 64, 512, 512], input.1088 float32 [1, 64, 256, 256], input.948 float32 [1, 128, 128, 128], input.832 float32 [1, 256, 64, 64], input.740 float32 [1, 512, 32, 32], input.656 float32 [1, 512, 16, 16]. Opset 13.
IS-Net / DIS: Xuebin Qin et al., Apache License 2.0 (https://github.com/xuebinqin/DIS). ONNX distributed by rembg (Daniel Gatis, MIT). https://github.com/danielgatis/rembg
Licence: Apache-2.0 — notice: https://github.com/xuebinqin/DIS/blob/main/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.
--- license: apache-2.0 tags: - skillsafe - browser - onnxruntime-web - background-removal - import --- # IS-Net (DIS) general-use background removal Browser-ready `import` artifacts for **background-removal**, produced by SkillSafe's reproducible converter ([`models/` in skillsafe.ai](https://github.com/skillsafe-admin/skillsafe.ai-website/tree/main/models)) 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://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx | | Upstream SHA-256 / commit | `v0.0.0` | | Recipe | `recipes/isnet-general-use.yaml` — sha256 `344b7fc0135d28c517f01921c74784fee61d4a31601d80b47d78ad582ada0781` | | Toolchain | Python 3.12.13, torch 2.10.0, onnx 1.23.0, onnxruntime 1.30.0 on Darwin 25.6.0 arm64 | | Converted | 2026-09-22T18:56:15+00:00 | ## Files | file | class | size | SHA-256 | |---|---|---|---| | `isnet-general-use.onnx` | registry | 170.37 MB | `60920e99c45464f2ba57bee2ad08c919a52bbf852739e96947fbb4358c0d964a` | `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 | |---|---|---|---| | `isnet-general-use.onnx` | input_image[1, 3, 1024, 1024] | output_image[1, 1, 1024, 1024], 1890[1, 1, 1024, 1024], 1891[1, 1, 1024, 1024], 1892[1, 1, 1024, 1024], 1893[1, 1, 1024, 1024], 1894[1, 1, 1024, 1024], input.1252[1, 64, 512, 512], input.1088[1, 64, 256, 256], input.948[1, 128, 128, 128], input.832[1, 256, 64, 64], input.740[1, 512, 32, 32], input.656[1, 512, 16, 16] | 1480.6 | ## Use in the browser ```js import * as ort from "onnxruntime-web"; const session = await ort.InferenceSession.create("https://huggingface.co/skillsafe-ai/isnet-general-use/resolve/main/isnet-general-use.onnx", { executionProviders: ["webgpu", "wasm"] }); ``` **Contract (isnet-general-use.onnx):** input `input_image` float32 `[1, 3, 1024, 1024]` → output `output_image` float32 `[1, 1, 1...
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