Better region PROPOSERS feeding the existing crop→SigLIP→max-over-bag heads (no
change to the learned-tagging approach; no per-tag cost — propose once, embed
each region, all heads in one matmul).
- detectors.py: lazy ultralytics YOLO wrapper, each proposer independently
optional + guarded (a bad weight spec / inference error self-disables that one,
logged, never breaks the worker). Weights resolve from an ultralytics name |
http(s) URL | "hf_repo::file", cached under HF_HOME. NMS merge so a figure two
detectors both find collapses to one crop.
- worker: figure boxes = imgutils detect_person ∪ general COCO person (merged)
→ CCIP + concept (anime + Western/realistic coverage); booru_yolo anatomy
components (head/cat-head/anatomy/…) → concept crops; comic panels → kind=
'panel' concept crops. Capped per frame (MAX_COMPONENTS/MAX_PANELS).
- config + compose: PERSON_WEIGHTS (default yolo11n.pt, works OOB),
ANATOMY_WEIGHTS + PANEL_WEIGHTS (operator sets booru_yolo URL + mosesb panel
hf::file; empty = off). ultralytics added to requirements.
- backend: image_region 'kind' doc notes 'panel'; no migration (free String,
and the bag scorer keys on a non-null siglip_embedding, not the kind, so any
SigLIP region joins the bag automatically).
Agent is outside CI — py-compiled here; operator tests on the GPU and checks
Western-vs-anime crop quality via /api/ccip observability.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Fast, read-only, indexed aggregates shaped for ANALYSIS (not the paged UI
directory, which is alphabetical + builds previews and timed out at 10 min on a
full count sweep).
- GET /api/tags/top — top tags by image count, desc. ?kind, ?limit (cap 500),
?min_count, ?source=all|human|manual|accepted|auto (human=manual+ml_accepted,
auto=head_auto+ccip_auto+ml_auto). One GROUP BY over image_tag (indexed on
tag_id).
- GET /api/tags/<id>/stats — per-tag dataset health: total + per-source counts
(manual/accepted/head_auto/ccip_auto), human vs auto rollups, rejection count,
and whether a trained head exists. Backs concept-readiness + source-split
analysis.
Plain-HTTP homelab posture, no auth change. Tests cover ranking, source filter,
min_count, the source breakdown, and 404.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Hygiene follow-up to the Camie retirement (#1189) — these were left inert to
bound that change; nothing reads them now. Migration 0068 drops:
- ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_
character/general (already dead pre-retirement — scoring uses per-head
thresholds), video_min_tag_frames (only the deleted video-prediction
aggregator used it).
- image_record: tagger_model_version (no writer), centroid_scores (dead JSON
cache, no reader).
Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant
+ video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding
card (interval + max frames only); importer no longer resets the dropped cols;
download_models drops the Camie fetch; stale CASCADE comments in cleanup_service
no longer name the removed tables. Tests updated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Heads + CCIP are the tag source and head auto-apply is the earned propagation.
The Camie tagger ran only to feed the allowlist bulk-apply (its ImagePrediction
rows had no other consumer), and the allowlist was a SECOND, un-earned auto-apply
path firing in parallel with heads on every accept — exactly the un-earned spray
the v2 pivot replaced. Retire both.
Behavior change: accepting a suggestion now applies the tag to THAT image only
(source='ml_accepted', a head-training positive) — it no longer allowlists +
fans the tag across the library via Camie. Propagation is heads' earned
auto-apply. (Loses instant cold-start propagation for booru-vocab tags; that was
un-earned and bypassed the precision gate.)
- tag_and_embed is now EMBED-ONLY (no Camie load/infer, no ImagePrediction
writes); backfill enqueues it for images with no embedding.
- Removed: services/ml/tagger.py, apply_allowlist_tags + helpers + daily beat +
every enqueue caller (accept/alias/merge/per-image), api/allowlist.py +
blueprint, ImagePrediction + TagAllowlist models/tables (migration 0067),
AllowlistTable.vue + allowlist store, the accept coverage-projection payload.
- AllowlistService gutted to accept/dismiss/undismiss/reject (the rejection store
the rail still needs); accept returns nothing, API returns {accepted, tag_id}.
- tag merge no longer repoints/triggers the allowlist; _keep_as_alias now keys on
ML-applied image_tag sources (incl. head_auto) instead of the allowlist.
- UI: MLBackfillCard relabelled to embedding-only; accept toast simplified;
MaintenancePanel drops the allowlist tile.
Left for a follow-up hygiene pass (now-inert, harmless): the dead settings
columns (tagger_store_floor, tagger_model_version, suggestion_threshold_*,
video_min_tag_frames), image_record.tagger_model_version, MLThresholdSliders
trim, and the Camie model download in download_models.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
The v2 pivot replaced per-tag SigLIP centroids with learned heads + CCIP.
Centroids were still recomputed (on every tag merge + a daily beat) but NOTHING
read them — suggestions come from heads+CCIP and apply_allowlist_tags applies
via Camie predictions, not centroids. Pure dead wiring; remove it.
Removed: CentroidService, recompute_centroid/recompute_centroids tasks, the
daily beat, POST /api/ml/recompute-centroids, the recompute-on-merge trigger,
the tag_reference_embedding table + model, the centroid_similarity_threshold +
min_reference_images settings (migration 0066), the CentroidRecomputeCard +
its store action + MaintenancePanel tile, and the centroid slider in
MLThresholdSliders. _keep_as_alias drops its vestigial has-centroid branch (the
allowlist branch already covers "could re-emit"); tag merge no longer clears a
table that no longer exists.
NOT touched (still live, parallel to heads): the Camie tagger, ImagePrediction,
and the allowlist bulk-apply — accepting a suggestion still allowlists + applies
it across the library. The tag-eval "centroid" baseline metric is unrelated
(in-memory) and stays. (image_record.centroid_scores JSON column also remains —
separate legacy field, its own micro-cleanup.)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.
- settings: embedder_model_name is now a setting (migration 0065) alongside the
existing embedder_model_version; both editable + validated (non-empty) in the
ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
model-agnostic), preferring the pre-downloaded local dir for the default so
existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
under the lease-announced model → POST /jobs/submit_embedding writes
image_record.siglip_embedding + siglip_model_version. The lease now announces
the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
version images; 'siglip' now re-embeds concept crops whose version != current
(so a swap re-triggers crops, not just the never-embedded back-catalogue). The
CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
churn the library at 512px) — the GPU agent owns version re-embeds. Daily
'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
image gated by siglip_model_version, concept regions by embedding_version) so a
mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
"Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.
Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Pure nearest-cosine piled near-identical images into the neighbour grid — a
reposted banner filled all 24 slots, and once you wandered into a B&W /
comic-panel cluster every neighbour was more of the same with no way back to
colour without the Random button (operator-reported, with screenshot).
similar() now over-fetches a wide candidate pool (5x the requested limit, cap
200), then diversifies down to `limit`:
- pHash near-duplicate collapse: drop candidates within 6 Hamming bits of the
anchor or an already-kept candidate, so a repost (and the anchor's own clones)
appears at most once.
- MMR re-rank: greedily pick for closeness-to-anchor minus similarity-to-already
-picked (lambda 0.55), so the result SPANS clusters instead of returning 40
variations of one image. Falls back to nearest-order on any failure / small
pool, so existing nearest-first behaviour is unchanged when there's nothing to
diversify.
Frontend forwardTarget drops the now-redundant skip-nearest-third hack (the list
is already diversified server-side) — plain random-over-unvisited gives the
variance now.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
bvandeusen