Field	Value
Hub id	`DotCheck/vermeer-image-v5`
Wire id	`inhouse@5`
Label	Vermeer
Artifact	`siglip2_base_patch16_224_linear_head_v5.npz`
Backbone	`google/siglip2-base-patch16-224` (Apache-2.0)
Head	trained linear / logistic on frozen SigLIP2 embeddings
Output	`p ∈ [0,1]` — P(AI-like); higher ⇒ more AI-like
Serve	CPU FastAPI (`POST /v1/analyze-image`); public clients → Express
Encode	client transport max side 256; processor → 224

Model description

Frozen SigLIP2-base vision tower + DotCheck head (*.npz). No fine-tune of the backbone in the served stack. This card documents the production image engine; prior CLIP ViT-B/32 @5 is archived and not loaded.

Files in this repo: README.md, LICENSE, NOTICE, CITATION.cff, and the .npz head file(s) listed above.

Architecture

text

JPEG/PNG bytes
  → resize (max side 256 at product edge)
  → AutoProcessor / SigLIP2 encode (224)
  → frozen embedding
  → linear/logistic head (npz)
  → p_AI

Shared SigLIP2 process with video (inhouse-video@2); separate head artifact.

Inference

Open weights: the live .npz head(s) in this repo (Apache-2.0), for use with the frozen upstream backbone named above. This is not a transformers AutoModel.from_pretrained("DotCheck/…") package.

Product scoring: Check or Pro API (below). Leviathan (shared memory and related product path) is not in these files.

HTTP (Pro API key dc_…; create in product Dashboard):

bash

curl -sS -X POST "https://dotcheck-server-c221c1f32c68.herokuapp.com/analyze-image" \
  -H "Authorization: Bearer dc_YOUR_KEY" \
  -F "file=@photo.jpg"

Guest UI: https://dotcheck.ai/check · contract: https://dotcheck.ai/api · gates PDF/tables: https://dotcheck.ai/docs

Response includes wire engine (inhouse@5) and engine_label (Vermeer).

Training data

Split	Content
Fit AI	Commercial-clean self-gen (SD family); no NC / GenImage / CIFAKE / CommunityForensics*
Fit real	Diversified Commons / Picsum + JPEG/size stress
Holdout AI	Kandinsky 2.2 (generator family withheld from fit)
Holdout real	Wiki / Commons-style reals (~200 / class in gate protocol)

Evidence: eval/results/quality_gates_v5.json · IMAGE_GATES_OK.

Evaluation

Metric	Target	Measured
mean P(AI) \| real	≤ 0.12	0.018
mean P(AI) \| AI	≥ 0.88	0.988
separation (AI−real)	≥ 0.55	0.970
bal_acc @ thr	≥ 0.92	0.9875

SSOT floats: repo Data.json / MODEL-CHOICE.md. Do not cite eval/candidates.yaml (stale).

Intended use

Binary AI-likeness scoring for still images in DotCheck inference.
Reproducible citation of the holdout table above.

Out of scope

Product scoring SLA / Leviathan / FUP via Hub download
Generator identification / provenance (optional Pro vendor confirm is a separate path)
Legal determinations of authorship

Limitations

Domain shift: heavy recompression, novel generators, adversarial edits.
Score = likeliness under this model, not a calibrated posterior over all generators.
Closed commercial gens not in holdout may differ; not measured here.

License

LICENSE — Apache License 2.0 for DotCheck heads in this repo. Upstream backbones: see NOTICE.

Citation

CITATION.cff. Prefer wire inhouse@5 / label Vermeer@5 + https://dotcheck.ai/docs.