modnetimage-segmentation · family: modnet
| Source | https://huggingface.co/litert-community/MODNet-LiteRT |
|---|---|
| License | apache-2.0 |
| Model file | 39a04456d74b97a711d1512656c76a1eaf0dc578cc44e6827963368f26354200.tflite — downloaded from the source at runtime; this site hosts no weights |
Preparing…
Every number is labeled with the environment that produced it — headless desktop Chromium is not a user’s phone, and results are only meaningful together with their environment.
| Backend | Status | Full delegation | Output match | Latency p50 (ms) | Environment | Verified | Provenance |
|---|---|---|---|---|---|---|---|
wasm_xnnpack | pass | — | — | 480.658 | mac-studio-m4-max · chromium 151.0.7922.34 headless · macOS · @litertjs/core 2.5.3 | 2026-08-11 | measured |
webgpu_mldrift | pass | yes | pass | 20.715 | mac-studio-m4-max · chromium 151.0.7922.34 headless · macOS · @litertjs/core 2.5.3 | 2026-08-11 | measured |
Full record (failure class, evidence, sweep config): raw sweep JSON · card.json · CARD.md
One-line prompt for your AI coding assistant (uses the litert-cookbook web skill):
Use the litert-cookbook web skill to build a LiteRT.js browser demo for modnet: load the model from https://huggingface.co/litert-community/MODNet-LiteRT/resolve/main/39a04456d74b97a711d1512656c76a1eaf0dc578cc44e6827963368f26354200.tflite (license: apache-2.0) and run it with @litertjs/core.
One-line prompt for Claude Code / Gemini CLI — re-runs the same sweep verification for this model, locally:
In the edge-compat repo, verify modnet with the sweep harness: write /tmp/catalog.csv with the header line model_id,source,input_spec,license,notes and the single row modnet,https://huggingface.co/litert-community/MODNet-LiteRT/resolve/main/39a04456d74b97a711d1512656c76a1eaf0dc578cc44e6827963368f26354200.tflite,1x3x512x512:float32,apache-2.0, — then run: cd web/sweep && npm ci && npm run sweep -- --catalog /tmp/catalog.csv --out out/ — and compare out/modnet.json with the sweep table on this page.