Card: weights and model code verified against the official MODNet release

NOTICE

@@ -1,6 +1,6 @@
 This repository contains a modified version of https://github.com/ZHKKKe/MODNet.
 
 Modifications by Meta Platforms, Inc.: conversion to the ExecuTorch program format (.pte) for the
-ExecuTorch WebGPU backend, and quantization as described in README.md.
+ExecuTorch WebGPU backend.
 
 The original work and these modifications are distributed under the license in LICENSE.

README.md

@@ -2,6 +2,7 @@
 license: apache-2.0
 library_name: executorch
 pipeline_tag: image-segmentation
+inference: false
 tags:
 - executorch
 - webgpu
@@ -12,14 +13,39 @@ tags:
 
 This repository contains an [ExecuTorch](https://github.com/pytorch/executorch) conversion of
 https://github.com/ZHKKKe/MODNet for the ExecuTorch **WebGPU** backend, so the model runs entirely in a web browser on
-the visitor's GPU. It powers the corresponding demo in the
-ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)).
+the visitor's GPU. It powers the corresponding demo in the ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)).
 
 - **Base model:** https://github.com/ZHKKKe/MODNet
 - **Format:** ExecuTorch `.pte` programs delegated to the WebGPU backend
 - **Quantization:** fp32 (as exported)
 - **Total size:** 24.9 MB
 
+## About the original model
+
+A real-time portrait matting network that separates people from the background without a trimap.
+
+- **Developed by:** Zhanghan Ke et al.
+- **Paper:** [MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition](https://arxiv.org/abs/2011.11961)
+- 8.7M parameters in the released checkpoint
+- Photographic portrait matting checkpoint
+
+For training data, evaluation results and full details, see the original model card: https://github.com/ZHKKKe/MODNet.
+
+## Intended use and limitations
+
+Cutting people out of photos or camera frames, for example to blur or replace the background.
+
+Trained for portraits: results degrade on non-human subjects, several overlapping people, or unusual poses and lighting.
+
+## What changed in this conversion
+
+- Fixed 256x256 input; the matte is upsampled to the frame size outside the program
+
+## Inputs and outputs
+
+- Input `[1, 3, 256, 256]` float32 RGB normalized to [-1, 1]: `(x / 255 - 0.5) / 0.5`
+- Output `[1, 1, 256, 256]` float32 alpha matte in [0, 1]
+
 ## Files
 
 | File | Size | SHA-256 |
@@ -30,20 +56,37 @@ Full checksums are in `manifest.json`. The playground verifies them after downlo
 
 ## Running it
 
-These programs need the ExecuTorch WebGPU runtime compiled to WebAssembly
-([backends/webgpu](https://github.com/pytorch/executorch/tree/main/backends/webgpu)). The easiest way to
-try the model is the ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)),
-which downloads these files once, caches them in the browser, and runs everything locally.
+These programs were exported with the ExecuTorch WebGPU backend
+([backends/webgpu](https://github.com/pytorch/executorch/tree/main/backends/webgpu)) and validated with the
+WebAssembly runtime build used by the ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)). That runtime build is not published yet; until it is,
+the playground is the supported way to run them. It downloads these files once, caches them in the browser,
+and runs everything locally.
+
+Requirements: a browser with WebGPU enabled.
 
 ## Provenance
 
 - **Weights:** `boopathiraj/MODNet` @ `a967175f8fd63de67a1c161d8daac966d2b57e3a`, file `modnet/pretrained/modnet_photographic_portrait_matting.ckpt`, sha256 `7c22235f0925deba15d4d63e53afcb654c47055bbcd98f56e393ab2584007ed8` (from a community mirror of the official release)
+- **Checked against the official checkpoint:** `modnet_photographic_portrait_matting.ckpt` is byte-identical to the official checkpoint linked from github.com/ZHKKKe/MODNet (sha256 `7c22235f0925deba15d4d63e53afcb654c47055bbcd98f56e393ab2584007ed8`), and the model code used for the export is identical to the official `src/models/` (one import path differs), checked by the project owner on 2026-10-16
 - **Exported with:** executorch 1.5.1+cpu, torch 2.14.1+cpu, torchao 0.18.0+cpu, transformers 5.19.0 (executorch git `3b6068392324`)
 - **Reproduce:** `python recipes/modnet.py --out out/modnet` (recipe in `export/`)
 - Re-running the recipe produces byte-identical files.
 
+## Citation
+
+If you use this model, please cite the original work:
+
+```bibtex
+@inproceedings{ke2022modnet,
+  title={MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition},
+  author={Ke, Zhanghan and Sun, Jiayu and Li, Kaican and Yan, Qiong and Lau, Rynson W.H.},
+  booktitle={AAAI},
+  year={2022}
+}
+```
+
 ## License
 
 This is a derivative of the upstream model and is distributed under the same terms
 (`apache-2.0`); see `LICENSE`.
-Modifications (conversion to the ExecuTorch format and quantization) by Meta Platforms, Inc.
+Modifications (conversion to the ExecuTorch format) by Meta Platforms, Inc.