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The noise model takes the best source the frame has: the body's measured table (the Canon EOS 6D's, from the library), the DNG's NoiseProfile, or the frame itself — read, row and column noise from its masked border, and only the shot gain estimated, from the quietest flat patches. Checked on 130 6D frames, the estimate is within 10 % from ISO 1000 up; the network loses under 0.3 dB for a sigma off by 15-20 %, so every Bayer body is eligible. Tiles of 1408 keep their central 1024 behind a 192-photosite halo, past the 185-photosite receptive field, and the frame is extended by reflection, which keeps every photosite's colour; a pattern that starts on another colour is read from one photosite up or left so the network sees RGGB, and nothing is cropped. The tests run every Bayer phase, tiled against whole, with a stand-in network of known reach. The model ships as models/denoise/mosaic-1408.onnx (LFS), trained in darkroom-denoise on the maintainer's own photographs, GPL like the code. denoise_raw runs a file end to end: on a 6D frame at ISO 8000 the result matches the training repository's own path to 2.5e-4 at worst, and takes 3.1 s on TensorRT fp16 (75 dB from f32) or 14.4 s on the CPU.
138 lines
7.6 KiB
Markdown
138 lines
7.6 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/` and the border filler in `inpaint/` are
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the other two, and the easiest — see the last two sections.
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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/dev/segmentation.md` §7 records the reasoning.
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## Class vocabulary — a caveat worth reading
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`docs/dev/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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## `inpaint/` — MI-GAN, MIT
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| File | Source | Trained on | Used by |
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|---|---|---|---|
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| `inpaint/migan-512.onnx` | `migan_512_places2.pt` from `https://github.com/Picsart-AI-Research/MI-GAN` (Sargsyan et al., ICCV 2023), **fine-tuned** in the `darkroom-infill` repository (2026-09-20, second model that evening: trained against MI-GAN's own discriminator) | Places2 by the authors, then ~7 400 of the maintainer's own photographs with border-shaped voids | the panorama border fill (FR-MRG-4) |
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The bare 512 generator at a fixed `1×4×512×512`, six operator types; the
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tiling, the context and the blend are Rust (`dr_pano::fill`). Since
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2026-09-20 the shipped file is the fine-tune (`docs/dev/panorama.md` §14),
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exported by `python -m infill.export` in `darkroom-infill`;
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`tools/export-migan.sh` still produces the stock generator from the upstream
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checkpoint, which the fine-tune starts from. The fine-tuned weights are a
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derivative of the MIT weights trained on photographs the maintainer owns,
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and carry the same MIT grant.
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**MIT, code and weights alike** — `LICENSE` and `LICENSE-WEIGHTS` in the
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repository, both read on 2026-09-19, both the plain MIT text with no further
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grant. GPL-compatible, store-compatible, nothing to read around: the cleanest
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position of any model here. The training set is Places2, a research dataset,
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but the weights are released under the repository's licence without a
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data-derived restriction (contrast the gaze models §7 of the requirements
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declined, and the InsightFace grant of D13).
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## `denoise/` — the mosaic denoiser, the project's own
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| File | Source | Trained on | Used by |
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| `denoise/mosaic-1408.onnx` | trained from scratch in the `darkroom-denoise` repository (2026-10-03, run `m2`, 60 000 steps) | 427 of the maintainer's own base-ISO Canon EOS 6D raws, with the 6D's measured noise added | the learned demosaic and denoise (FR-DEV-3g) |
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A U-Net of plain 3×3 convolutions, ReLU, strided and transposed
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convolutions and additive skips — no third-party architecture code or
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weights — at a fixed `1×1×1408×1408` for `mosaic` and `sigma`, exported by
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`python -m denoise.export` in `darkroom-denoise`. Trained only on
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photographs the maintainer owns, so the weights carry no grant but the
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project's own: GPL-3.0-or-later, like the code (denoise.md §10).
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