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Twelve real frames from the fixture set now align in 4.5 s — 4.4 s of matching, 118 ms of bundle adjustment — where the first run took 51 s and left the first two frames out. The matcher computes each pair's similarity matrix once, across the cores, with a dot product written to vectorise; both nearest-neighbour directions read it. The frames that failed were portrait: fitted into the landscape input they used 512 of 1024 px, and their thin overlap did not survive at half resolution. The same weights are now exported at 768×1024 as well and the detector picks the shape by aspect. The example aligns from embedded previews and draws the set on a cylinder; on the fixture the sweep is 152° at a fitted 47.9 mm against the EXIF's 50, RMS 1.5 px, and the overlaps show no ghosting.
102 lines
5.3 KiB
Markdown
102 lines
5.3 KiB
Markdown
# Model weights — licensing
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Two Ultralytics checkpoints ship here, both exported by
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`tools/export-seg-model.sh`, each with its class vocabulary written out by the
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same script:
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| File | Checkpoint | Trained on | Used by |
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|---|---|---|---|
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| `segment/yolo26n-seg.onnx` | `yolo26n-seg.pt` | COCO, 80 *thing* classes | local adjustments, subject selection |
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| `scene/yolo26s-sem-ade20k.onnx` | `yolo26s-sem-ade20k.pt` | ADE20K, 150 classes | the scene tab's per-category grades |
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Both come from `https://huggingface.co/Ultralytics/YOLO26`. The face weights in
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`face/` are a separate matter with a separate grant — see `face/README.md`.
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The keypoint weights in `keypoints/` are a third, and the easiest — see the
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last section.
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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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**This was true when written and is not any more.** Checked 2026-08-21, no
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YOLO/ADE20K combination existed: Ultralytics shipped YOLO26-seg on **COCO**
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only, and the one HuggingFace repository claiming otherwise
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(`laxmacl/yolov8-ade20k`) was empty. Re-checked 2026-08-30: Ultralytics now
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ships a `semantic` task with ADE20K checkpoints
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(`https://docs.ultralytics.com/tasks/semantic`), and `yolo26s-sem-ade20k` is
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what `scene/` holds.
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So the two vocabularies divide the work rather than compete:
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- **`segment/`, COCO, 80 things.** Separates *instances* — clicking one of
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three people selects that person. This is what local adjustments need, and a
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semantic model cannot do it: it would return one "person" region covering all
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three.
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- **`scene/`, ADE20K, 150 classes.** Labels every pixel, including the *stuff*
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COCO has no word for — sky, vegetation, water, mountain, wall. This is what
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the scene tab's per-category grades need, and it does not care that instances
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are merged, because a per-category grade applies to the whole category.
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Neither replaces the other. Keeping both is the deliberate choice.
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The loader treats each vocabulary as model metadata rather than compiled-in
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knowledge, which is what made adding the second model a file plus a descriptor
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rather than a code change — as this document predicted it would be.
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## `keypoints/` — XFeat, Apache-2.0
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| File | Source | Trained on | Used by |
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|---|---|---|---|
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| `keypoints/xfeat-1024.onnx` | `weights/xfeat.pt` from `https://github.com/verlab/accelerated_features` | MegaDepth + synthetic warps, by the authors | panorama alignment (FR-MRG-8), landscape frames |
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| `keypoints/xfeat-768.onnx` | the same weights | — | the same, portrait frames |
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Exported by `tools/export-xfeat.sh` at fixed grayscale inputs of 1024×768
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and 768×1024 — the same weights twice, because tract needs a static shape
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and a portrait frame in a landscape input wastes half of it. Only the
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convolutional network is in each file; the keypoint decoding is Rust.
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**The repository and its weights are Apache-2.0**, read on 2026-09-19 from the
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`LICENSE` at its root, with no separate grant on the checkpoint and no
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non-commercial clause anywhere in the tree. Apache-2.0 is GPLv3-compatible
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one way — code and weights under it may be combined into a GPLv3 work — so
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this is neither the InsightFace situation (D13, a use restriction that binds
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every user) nor the Ultralytics one (D14, where the combined work becomes
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AGPL). It is the licence position this document would have wanted for every
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model in it, and it was chosen over stronger detectors partly for that reason:
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SuperPoint and SuperGlue are non-commercial, R2D2 and SiLK are CC BY-NC.
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The training data is the authors' concern, not a licence on the weights:
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XFeat trains on MegaDepth, which is itself a research dataset, but the weights
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are released under the repository's licence without a data-derived
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restriction — unlike the gaze models §7 of the requirements declined, where
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the dataset licence restricts models trained on it by name.
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