remove-ai-watermarks/docs/photo-classify-training.md

Photo classifier training and data

How the 2026-08-31 freeze was fit, what a retrain needs, and where those files come from. Inference is photo-classify.md. The campaign log is ai-generated-image-classifiers.md.

The shipped package does not include the catalog or the embeddings. Fitting the heads again uses the cache pack. The original pixels still have to be kept: a missing embedding, a new backbone, or an audit all need the files.

Retrain pack (caches)

CPU retrain of both heads needs sha256-keyed matrices and the catalog of roles. That step does not open JPEG or PNG files. It is not permission to drop the pixels.

File	Role
catalog.json	32,690 rows: sha256, roles, optional family / public source
embeddings-by-sha256.npz	CLIP-L-ft 768-d vectors for Model 1
extra-clip-l.npz	CLIP-L extras for the 150 Flickr UI hashes
features-124d.npz	124-d residual vectors for Model 2
probe-weights-clip-l-ft.npz	Ridge mean, scale, weights, thr_oi_1pct
meta-split.json	Meta train 217, hold-out v2 79, hold-out v3 198

Run:

uv run python scripts/retrain_photo_classify.py --pack DIR --out DIR

Seeds are fixed: detector 20260940, provider 20260956. Two consecutive runs must be byte-identical.

Not in the published retrain pack: raw photographs, COCO/Open Images/Kodak binaries, CORD, Danbooru. Those files stay in their public corpora or in the private freeze archive. Pixel rebuild of a missing embedding still needs clip-l-ft.pt and the original file.

Where the rows come from

Identity is always sha256 of the file bytes.

Public, named corpora

Role	Source
detector_photo_train, detector_photo_coco_hold	COCO 2017 validation stills
detector_open_images_*, detector_photo_dev_oi	Open Images photographs. Locked fresh never enters train
detector_photo_kodak	Kodak lossless true color, 24 files
detector_photo_picsum_eval	Picsum
detector_flux_hf_hold	Hugging Face FLUX stills, hold-out
Flickr UI cell (150)	Openverse license_type=all-cc, host live.staticflickr.com only
Meta hold-out v3 (198)	Meta Muse Image API, eval-only, not in train

In-house labeled photographs, not redistributed

Provider OpenAI, Google, and TC260 train and test rows, and most detector AI train and test rows, are labeled photographic generations held privately. The published pack carries only their sha256, roles, and precomputed vectors. It does not carry the pixels.

Out of the freeze train

CORD receipts, FUNSD forms, Danbooru, charts, and map tiles were measured as failure domains. They are not Model 1 train. A future universal router treats them as other domains, not as extra CLIP-L negatives.

What the freeze script does

Historically freeze_both_models.py built the catalog from local paths, then trained. That campaign code is private and is not in this repository. The public entry is scripts/retrain_photo_classify.py, which skips catalog construction and fits the same heads from the pack above.

1. Load CLIP-L-ft vectors by sha256. Fit the 768-512-128-1 MLP on detector_ai_train plus extras versus detector_photo_train plus struct. Threshold is the 1.67% quantile on detector_photo_dev_oi.
2. AND that MLP with the frozen ridge in the probe file. DEFINITELY is both.
3. Load 124-d vectors. Fit one-vs-rest focal heads for openai, google, tc260, meta_muse_image, no_ai. Margin 0.30. The public class for the Muse Image head is muse-image. tc260 is the China AIGC label standard, mixed producers, not one company.
4. Repeat with the same seeds. Compare bytes.

CLIP-L fine-tune of the last two vision blocks is earlier, seed 20260822, and is not repeated by the retrain script. The backbone file clip-l-ft.pt is an inference and embedding dependency, not a retrain-pack input.

Next retrain

Keep the current tc260 head until then. The next provider-head fit should split that catalog by ContentProducer (Doubao, Jimeng, Qwen, Kling, and the rest) instead of one label-standard class. Do not treat a tc260 score as a named manufacturer.

The 2026-09-02 drift campaign set two preconditions for adding renderer drift rows to that fit. First, scale: 14 openai drift rows recovered exactly one near-miss row in leave-one-out while regressing a sibling cell through the shared negative pools (google -10 or meta -6 depending on variant), so drift rows should enter together with enough mass to hold every cell flat, not as a standalone patch. Second, scope: 6 of 12 Aug rows failed before the provider stage, so a drift fix that covers the whole cell needs Model 1 in the loop. The ContentProducer split still needs metadata exported from the app database.