Upload 2 files

bc20f53 verified 2 days ago

1.75 kB

license: apache-2.0
library_name: onnx
pipeline_tag: image-segmentation
tags:
  - background-removal
  - onnx
  - onnxruntime-web
  - dichotomous-image-segmentation

ISNet general-use (fp16 ONNX) for in-browser background removal

A half-precision (fp16) copy of the ISNet general-use model, used by techyCoderWorld's Image Studio to remove image backgrounds entirely in the visitor's browser with ONNX Runtime Web (WebGPU or WebAssembly). Images never leave the device.

Source and licence

Model: ISNet from Highly Accurate Dichotomous Image Segmentation (DIS), Xuebin Qin, Hang Dai, Xiaobin Hu, Deng-Ping Fan, Ling Shao, Luc Van Gool (ECCV 2022). Code and weights: https://github.com/xuebinqin/DIS, released under the Apache License 2.0.
ONNX export used as the starting point: isnet-general-use.onnx distributed by the rembg project (https://github.com/danielgatis/rembg), SHA-256 60920e99c45464f2ba57bee2ad08c919a52bbf852739e96947fbb4358c0d964a.

Changes made

Weights and internal computation converted from float32 to float16 with onnxconverter-common 1.14.0 (keep_io_types=True), so inputs and outputs stay float32.
No retraining or other changes. Output matches the float32 model to within fp16 precision.

File

File	SHA-256	Size
`model_fp16.onnx`	`d27e782d3bec1c787c5b6c612a1aae8526373a721540d4046939e8a75d2db949`	90662600 bytes

Input and output

Input input_image: float32 [1, 3, 1024, 1024], RGB, each pixel divided by the image's maximum value, then minus 0.5.
Output: float32 [1, 1, 1024, 1024]; min-max normalise to get the foreground mask (0..1).

Licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0