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README.md

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 ---
 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

model_fp16.onnx

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+oid sha256:d27e782d3bec1c787c5b6c612a1aae8526373a721540d4046939e8a75d2db949
+size 90662600