`models/LICENCE.md` recorded, on 2026-08-21, that no YOLO model trained on
ADE20K existed in usable form — the stuff classes photography cares about,
sky and vegetation and water, had no model to come from. Re-checked
2026-08-30: Ultralytics now ships a `semantic` task with ADE20K
checkpoints, so `models/scene/` holds `yolo26s-sem-ade20k`.
This is an addition, not a replacement. A semantic model labels every
pixel but merges same-class pixels into one region, so it cannot tell
three people apart — which is exactly what clicking a subject needs, and
exactly what `segment/`'s COCO instance model already does. The scene tab
grades per category and does not care that instances are merged. Keeping
both is the point.
## The export is truncated, deliberately
Ultralytics ends the graph with `Resize -> ArgMax -> Cast` and hands back
a `[1, 640, 640]` u8 label map. The script cuts that tail and exposes the
classifier's `[1, 150, 80, 80]` f32 logits instead, for two reasons.
Cost: the Resize materialises 150 x 640 x 640 x f32, 246 MB, and ArgMax
then reduces across the channel axis, striding 409,600 elements per
comparison. On one loaded machine the full graph ran ~1160 ms against
~500 ms truncated — roughly four fifths of the time spent on work the
application discards. Those numbers were measured under contention and
are upper bounds, but the ratio is structural.
Softness: ArgMax destroys the per-class scores, and the scene tab needs
them. Softmax over the 150 channels, summed within each photographic
category, yields per-category weights summing to 1 at every pixel.
Feathering a partition of unity cannot double-grade a boundary, whereas
feathering hard labels outward from two adjacent categories paints both
grades into the overlap and haloes every horizon.
The discarded upsample was never information: the graph's true spatial
resolution is the 80x80 logit grid, and the application can resample from
that itself.
The tail is matched by op type and asserted before cutting, so an
upstream graph change fails loudly in the exporter rather than quietly
shipping a differently-shaped model.
Nothing reads these weights yet — the decode path, the category
descriptor grouping 150 classes into ~8 photographic ones, and the scene
tab are still to come. At 24 MB this model also wants the runtime-asset
treatment `models/face/` already gets on Android rather than
`include_bytes!`; embedding it would put ~35 MB of weights in the binary.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The weights were in two places: face detection and recognition in
`models/face/`, segmentation in `core/dr-segment/models/`. Nothing was
wrong with either path, but between them there was nowhere to look to
answer "how much model does this application carry", and that number is
about to start growing.
So the crate-local copy moves up beside the other. `models/` now holds
`face/` and `segment/`, and a `du -sh` of one directory is the whole
answer.
No content changes: the .onnx and its vocabulary are byte-identical, and
`LICENCE.md` moves up a level to cover the tree rather than one crate.
The LFS pattern in `.gitattributes` is `*.onnx` and already matched both
locations, so only its comment needed the new path.
`include_bytes!` is relative to the source file and `build.rs` runs with
the crate root as its working directory, which is why the two paths climb
a different number of levels.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
dtourolleandClaude Opus 5