dtourolle
4f31123b0c
faces.md §12.3 measured what the cheapest detector costs: the small faces in every group shot, and a dog embedded a dozen times. Which trade is right depends on the machine doing the sweep — a desktop left overnight and a tablet on a battery want different answers — so the detector is now a per-device setting, Fast / Balanced / Thorough on the settings page beside the indexing button, persisted with the rest of the settings file. A detector is half of a model id. Every face, marker, shard and calibration is keyed on faces.model_id precisely so that a model change is a new id and a re-index rather than a silent change under existing data, and a detector change is a model change: it decides which faces exist and where the landmarks that align them land. So each choice names its own pipeline. 500M keeps the bare "w600k_mbf" every existing library was written under, so an upgrade disturbs nothing; the others are qualified. Choosing one restarts coverage from zero under the new id, the sweep re-detects, confirmed names carry across by box overlap, and the sync shards are keyed by the same id so a peer on another setting neither adopts nor pollutes them. The library controller carries the id into the sync the same way it carries the cache budget, because the sync starts from places that have no settings in reach. All three shape-fixed exports ship — APK, Arch, Flatpak — since a tablet has no other way to obtain the one it was not installed with; the APK grows by twenty megabytes for the choice.
2.3 KiB
Face models
The shape-fixed SCRFD detectors and the ArcFace/MobileFaceNet embedder, in LFS. One copy, packaged by every platform:
| Platform | How it ships | Where it lands |
|---|---|---|
| Android | assemble-apk.sh bundles them as APK assets; android_main unpacks on first launch |
the shared user data directory |
| Arch | PKGBUILD installs them |
/usr/share/darkroom/models/ |
library::face_models searches the account's own directory, then the shared user directory, then
$XDG_DATA_DIRS — so a pair the user placed by hand always outranks the packaged one.
scrfd_500m_640.onnx 2.5 MB "Fast" in settings — what every library was indexed with until 0.12
scrfd_2.5g_640.onnx 3.3 MB "Balanced" — 14% more faces for 12% more time (faces.md §12.3)
scrfd_10g_640.onnx 17 MB "Thorough" — a further 12% for 3× the time
arcface_mbf_b1.onnx 13 MB
Which detector runs is a per-device setting (FaceSettings::detector); all three are installed so
the choice exists on every platform. Each is its own faces.model_id, so changing it re-indexes.
A clone without git-lfs gets a ~130-byte pointer where each model should be. Both packagers check
for exactly that and refuse, rather than shipping the pointer and failing inside tract on the user's
machine. Fix it with git lfs pull.
These are not what InsightFace ships. They came from buffalo_sc.zip, buffalo_m.zip, buffalo_l.zip
and buffalo_s.zip on the InsightFace v0.7 release with their input dimensions pinned, because tract
cannot parse any of the graphs while they are dynamic:
./tools/fix-face-model-shapes.sh det_500m.onnx models/face/scrfd_500m_640.onnx --input input.1=1,3,640,640
./tools/fix-face-model-shapes.sh det_2.5g.onnx models/face/scrfd_2.5g_640.onnx --input input.1=1,3,640,640
./tools/fix-face-model-shapes.sh det_10g.onnx models/face/scrfd_10g_640.onnx --input input.1=1,3,640,640
./tools/fix-face-model-shapes.sh w600k_mbf.onnx models/face/arcface_mbf_b1.onnx --dim None=1
The weights carry a non-commercial research-only grant. They are here because this is a private repository and self-installed builds; they come back out before anything is published, and the restriction binds whoever uses the app, not only the project. docs/faces.md §2.2a is the decision.