---
license: apache-2.0
library_name: executorch
pipeline_tag: image-segmentation
inference: false
tags:
- executorch
- webgpu
- on-device
- browser
---
# MODNet portrait matting for ExecuTorch WebGPU

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)).

- **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 |
|---|---|---|
| `modnet_webgpu.pte` | 24.9 MB | `0c2ff92e26087e98…` |

Full checksums are in `manifest.json`. The playground verifies them after download.

## Running it

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) by Meta Platforms, Inc.