dtourolle
0da8271836
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.
55 lines
2.7 KiB
Markdown
55 lines
2.7 KiB
Markdown
# Model weights — licensing
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`yolo26n-seg.onnx` is exported from Ultralytics YOLO26n-seg
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(`https://huggingface.co/Ultralytics/YOLO26`, `yolo26n-seg.pt`) by
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`tools/export-seg-model.sh`. `yolo26n-seg.classes.json` is that checkpoint's
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class vocabulary, written out by the same script.
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## The grant
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**Ultralytics releases YOLO under AGPL-3.0**, and the weights carry the same
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grant as the framework — the HuggingFace repository declares `agpl-3.0` for the
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checkpoints themselves, not merely for the training code. A commercial licence
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is offered separately; DarkRoom does not use it and does not need it.
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## What that means for DarkRoom
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DarkRoom is GPL-3.0-or-later. **GPLv3 §13 explicitly permits combination with
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AGPL-3.0 code**, so redistributing these weights inside this repository is
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allowed — this is *not* the situation the InsightFace "buffalo" weights would
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have created, where a non-commercial research grant is simply incompatible with
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the project's licence and with F-Droid, Flatpak and Play distribution
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(NFR-COMPAT-2, D13).
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The consequence, and it is a real one: **the combined work is effectively
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AGPL-3.0.** §13's permission runs one way — the AGPL's §13 network-use condition
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attaches to the portion under that licence. For a local-first desktop and
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Android photo editor that condition has no practical bite, because there is no
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network service offering the combined work to remote users. It would acquire
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bite the moment any hosted or server-side rendering appeared, and that is the
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thing to remember rather than rediscover.
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This was decided deliberately (D14), not arrived at by accident, and
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`docs/segmentation.md` §7 records the reasoning.
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## Class vocabulary — a caveat worth reading
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`docs/segmentation.md` §4 specified YOLO **pretrained on ADE20K**, whose 150
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classes include the *stuff* categories that matter most in photography — sky,
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vegetation, water, wall, mountain.
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**No such model exists in usable form.** Checked 2026-08-21: Ultralytics ships
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YOLO26-seg trained on **COCO**, whose 80 classes are all *things* — person,
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dog, car, bird, potted plant — and the one HuggingFace repository claiming a
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YOLO/ADE20K combination (`laxmacl/yolov8-ade20k`) is empty. ADE20K semantic
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models do exist, but as SegFormer/OneFormer/MaskFormer transformers, not YOLO.
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So the shipped vocabulary selects **subjects**, not **stuff**. "Select the
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person" works; "select the sky" does not come from the model and must come from
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the watershed hierarchy instead. That is a narrower arm B than §4 assumed, and
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it raises rather than lowers the importance of arm C.
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The loader treats the vocabulary as model metadata rather than compiled-in
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knowledge, so adding a stuff-class model later is a file plus a descriptor, not
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a code change.
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