The engine knew f32 and int8, and gave the Hexagon int8 for every role it
served. Measured on the tablet itself (inference.md §1.5), int8 lost
5% of the detector's faces at 40-80 px, moved the landmarks 1.5 px,
emptied the segmenter's scores and cost the denoiser 5-9 dB; fp16 the HTP
refuses outright. `Form` gains A16W8 and A16W16, and `Rung::form` now
names one per role: detectors and landmarks A16W8, the segmenter, scene
model, border filler and denoiser A16W16, XFeat int8. The embedder and
the eye classifiers stay on the CPU.
Each loader resolves its `<stem>.<form>.onnx` sibling; the segmenter and
XFeat, compiled into the binary, embed their quantised forms on Android
only and pick through `choose_embedded`. The probe, the compile step and
the cache fingerprint follow the form instead of assuming int8. Detectors
on the new form write `scrfd_*_a16+w600k_mbf`, and `model_ids` answers
for all three spellings.
On the tablet (ORT 1.29 + QNN 2.42), each shipped file against f32 on the
same inputs, and against the CPU's f32 time:
SCRFD 500m/2.5g/10g A16W8 100% of faces in every band 4.2/5.1/9.0 ms vs 17/56/198
landmarks A16W8 0.25 px in the 192 crop 0.5 ms vs 2.8
YOLO26n-seg A16W16 98.2% found, mask IoU 0.994 12.9 ms vs 90
scene model A16W16 98.9% of cells agree 15 ms vs 151
MI-GAN A16W16 41 dB from f32 in the fill 87 ms vs 488
XFeat int8 pano alignment 0.45 px (f32's own spread 0.41) 6.5 ms vs 58
denoiser A16W16 0.00 dB at every ISO 95 ms vs 1510 a tile
Face numbers are over public COCO val2017 photographs, not a library.
The APK carries the siblings (BUNDLED 15 -> 19; the old int8 detectors
removed), about 43 MB more. The Windows installer and its CI count skip
them; the Arch and Flatpak packages list their files and never had them.
The ladder example takes a role per model, which is how the per-role
forms above were seen landing on the NPU from the real probe.
docs/ had 26 developer documents flat beside the manual, and the two
audiences are very differently sized: most readers want the manual and
the gesture reference, a few want the register, the designs and the
measurements. The manual and gestures.md stay at the top; everything for
someone changing the code moves to docs/dev/, and the two documents that
name their own successors — the v0.1 milestone and the UI-refinement plan
— go to docs/dev/archive/ rather than being deleted, since both are still
cited. docs/README.md is the index, users first.
Every reference follows: code comments, Cargo manifests, the workflows,
the pre-commit hook, the bench and traceability tools (which locate the
repo root by docs/dev/requirements.md now), packaging, the Docker READMEs,
CLAUDE.md, CONTRIBUTING.md and the README. The matrix links one level
deeper and is regenerated. Links out of the moved documents into the tree
gain a level; a link checker over every Markdown file finds none broken.
The first int8 files found no faces at all, and for two reasons the
tool now guards against. The calibration set was landscape photographs
with no faces in them, so the score head's ranges had never seen the
face regime; the set is now proxies from the library itself. And ONNX
Runtime's strided and moving-average calibration modes both degrade
these graphs measurably (a quarter of the faces at eight images, none
at ninety-six), while driving the calibrator in chunks by hand gives
ranges identical to a single pass — so the tool does that, four images
at a time, and feeds quantize_static through its range cache.
Measured against f32 over 400 proxies (docs/inference.md §10.1): the
10g form finds every face above 32 px the f32 form finds; 500m and
2.5g find 96%, and what they lose sits at a median confidence of 0.52
against the 0.50 threshold. Shipped with the number on record.
The Android unpack list gains the three int8 files; without that the
tablet never saw them. D13's runtime half records the reopening.
tools/quantise-models.sh writes the QDQ form QNN's HTP backend takes
whole: opset 17, per-channel int8 weights, uint8 activations, ranges
from running the f32 graph over photographs fed exactly as the app
feeds them. The calibration is strided, four images at a time, because
every ONNX Runtime calibrator holds each image's whole set of
activations until it folds them — a gigabyte an image on the 10g
detector, and an OOM kill with no message when folded once at the end.
Release-time, never on the device (docs/inference.md §5): it needs
real photographs and a person reading the recall measurement that
gates whether each file is offered.
2d106det for the eye contours, OCEC for open or closed, SGC for
sunglasses — all three pinned to a batch of one by the same script as
the pair, and installed by every packager so the eyes-open filter works
out of the box. The two classifiers are MIT, code and weights; the
README records their provenance, SGC's undocumented training set, and
the hashes as fetched and as shipped.
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.
The Android bundling landed the weights under that platform's asset directory,
which was the wrong home the moment a second packager wanted them. `makepkg -si`
produced a desktop install with no model at all — the same "no face model is
installed" the phone used to show, for the same reason: nothing put the files
anywhere the app looks.
So `models/face/` at the root is the one copy, and both packagers read it:
assemble-apk.sh bundles it as APK assets, and the PKGBUILD installs it to
/usr/share/darkroom/models. Both refuse an LFS pointer rather than shipping a
130-byte file that fails inside the graph loader on a user's machine.
`face_models` now searches three places, most specific first: the account's own
directory, the shared user directory, then $XDG_DATA_DIRS. So a packaged pair is
found automatically and a pair the user placed by hand still outranks it — which
is what keeps a deliberate choice of weights from being overridden by an
upgrade.
$XDG_DATA_DIRS rather than a hard-coded /usr/share: that is the variable a
distribution, a prefix install or a Nix-style store already sets to say where
its data went, and its documented default is exactly the two paths that would
otherwise have been hard-coded. Empty on Android, which has no such directories
— there the APK's copy is unpacked into the shared user directory instead,
because an asset inside a package is not a path anything can read from.
Verified: the APK still carries both models at assets/models/, the PKGBUILD
parses and installs from the new path, 467 tests pass.
Includes the pkgver 0.6.0 → 0.7.0 bump that was already sitting uncommitted in
the working tree.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
dtourolle
