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Local masking needs to know where an image's regions are. The watershed spike (S15 arm A) found the boundaries but had no idea what any of them enclosed; its coarse levels were geometric accidents. This adds the other half and the thing that joins them. `core/dr-segment` is where region reasoning now lives — the hierarchy moves out of `dr-gpu`, which keeps only the pixel passes that are genuinely shaders. The new crate is device-free and, without its default features, model-free too: 20 of its tests need neither an adapter nor 11 MB of weights. Arm B runs YOLO26n-seg through `ort`. D13 framed inference as a choice between `ort`'s C++ runtime and the pure-Rust dependency policy; that was a false choice. `ort`'s `alternative-backend` feature unlinks the C entirely and `ort-tract` supplies the API from tract, which is pure Rust. Measured before committing to it: zero unsupported operators, 420 ms for 640x640, and correct masks on bus.jpg. No NDK problem to solve, so D13's largest tolerated exception is not needed. Arm C is `prior.rs`, and it ships because the two arms fail in opposite directions. Instance membership re-weights the merge saddles, so region pairs the model believes share an object merge early and pairs straddling its edge merge late. No boundary moves — only the order in which they dissolve — which is how the result stays pixel-accurate at every level while its coarse levels become named things. Two things the spec assumed that turned out to be false, both recorded in models/LICENCE.md: there is no usable ADE20K-trained YOLO, so the shipped vocabulary is COCO's 80 subjects and *stuff* like sky and foliage must come from arm A; and tract cannot parse a dynamic-shape export, so the graph's input is fixed and tiling is the only route to more semantic resolution. Weights are AGPL-3.0, which GPLv3 §13 permits and which makes the combined work effectively AGPL. Deliberate, not accidental. They live in Git LFS, and a build script fails with an instruction rather than embedding a pointer file when the clone lacks them.
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Model weights — licensing
yolo26n-seg.onnx is exported from Ultralytics YOLO26n-seg
(https://huggingface.co/Ultralytics/YOLO26, yolo26n-seg.pt) by
tools/export-seg-model.sh. yolo26n-seg.classes.json is that checkpoint's
class vocabulary, written out by the same script.
The grant
Ultralytics releases YOLO under AGPL-3.0, and the weights carry the same
grant as the framework — the HuggingFace repository declares agpl-3.0 for the
checkpoints themselves, not merely for the training code. A commercial licence
is offered separately; DarkRoom does not use it and does not need it.
What that means for DarkRoom
DarkRoom is GPL-3.0-or-later. GPLv3 §13 explicitly permits combination with AGPL-3.0 code, so redistributing these weights inside this repository is allowed — this is not the situation the InsightFace "buffalo" weights would have created, where a non-commercial research grant is simply incompatible with the project's licence and with F-Droid, Flatpak and Play distribution (NFR-COMPAT-2, D13).
The consequence, and it is a real one: the combined work is effectively AGPL-3.0. §13's permission runs one way — the AGPL's §13 network-use condition attaches to the portion under that licence. For a local-first desktop and Android photo editor that condition has no practical bite, because there is no network service offering the combined work to remote users. It would acquire bite the moment any hosted or server-side rendering appeared, and that is the thing to remember rather than rediscover.
This was decided deliberately (D14), not arrived at by accident, and
docs/segmentation.md §7 records the reasoning.
Class vocabulary — a caveat worth reading
docs/segmentation.md §4 specified YOLO pretrained on ADE20K, whose 150
classes include the stuff categories that matter most in photography — sky,
vegetation, water, wall, mountain.
No such model exists in usable form. Checked 2026-08-21: Ultralytics ships
YOLO26-seg trained on COCO, whose 80 classes are all things — person,
dog, car, bird, potted plant — and the one HuggingFace repository claiming a
YOLO/ADE20K combination (laxmacl/yolov8-ade20k) is empty. ADE20K semantic
models do exist, but as SegFormer/OneFormer/MaskFormer transformers, not YOLO.
So the shipped vocabulary selects subjects, not stuff. "Select the person" works; "select the sky" does not come from the model and must come from the watershed hierarchy instead. That is a narrower arm B than §4 assumed, and it raises rather than lowers the importance of arm C.
The loader treats the vocabulary as model metadata rather than compiled-in knowledge, so adding a stuff-class model later is a file plus a descriptor, not a code change.