README.md

metadata

license: apache-2.0
library_name: executorch
pipeline_tag: image-segmentation
tags:
  - executorch
  - webgpu
  - on-device
  - browser

MODNet portrait matting for ExecuTorch WebGPU

This repository contains an 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).

• 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

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 need the ExecuTorch WebGPU runtime compiled to WebAssembly (backends/webgpu). The easiest way to try the model is the ExecuTorch WebGPU Playground (launching soon on executorch.ai), which downloads these files once, caches them in the browser, and runs everything locally.

Provenance

• Weights: boopathiraj/MODNet @ a967175f8fd63de67a1c161d8daac966d2b57e3a, file modnet/pretrained/modnet_photographic_portrait_matting.ckpt, sha256 7c22235f0925deba15d4d63e53afcb654c47055bbcd98f56e393ab2584007ed8 (from a community mirror of the official release)
• 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.

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.