bvandeusen
7d3a3b4a83
Milestone 139 raised head_auto_apply_precision 0.97→0.98; operator confirmed the general-tag confidence was already well tuned, so revert that. The support floor (min_positives 30→50) and CCIP match confidence (0.92→0.95) stay. Migration 0081 (not yet deployed) edited to drop the precision bump. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
177 lines
8.3 KiB
Python
177 lines
8.3 KiB
Python
"""MLSettings — single-row table holding ML pipeline tunables."""
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from datetime import datetime
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from sqlalchemy import (
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Boolean,
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CheckConstraint,
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DateTime,
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Float,
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Integer,
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String,
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func,
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)
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from sqlalchemy.orm import Mapped, mapped_column
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from .base import Base
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class MLSettings(Base):
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__tablename__ = "ml_settings"
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# Bare name — Base.metadata's naming convention prepends ck_<table>_,
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# producing the final ck_ml_settings_singleton (matches migration 0003).
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__table_args__ = (CheckConstraint("id = 1", name="singleton"),)
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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# CPU whole-image embedding (B3, operator 2026-07-02). The ml-worker's ONLY
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# processing role is the embed fallback for stacks WITHOUT a GPU agent — ON
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# by default so a fresh install works with no agent. Stacks that run the
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# agent and drop the ml-worker container turn this OFF so import hooks stop
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# queueing embed work nothing will consume (the daily GPU 'embed' backfill
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# covers those images instead).
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cpu_embed_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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# Video embedding (#747). Sample one frame every N seconds (fixed CADENCE, not
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# a fixed count) so coverage reflects real screen time regardless of length;
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# cap the total so a long video can't explode into hundreds of embeds. The
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# per-frame SigLIP embeddings are mean-pooled. Operator-tunable.
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video_frame_interval_seconds: Mapped[float] = mapped_column(
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Float, nullable=False, default=4.0
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)
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video_max_frames: Mapped[int] = mapped_column(
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Integer, nullable=False, default=64
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)
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# Tagging-v2 head training (#114). The head is the suggestion source that
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# LEARNS from the operator's tags (replacing Camie + centroid). A concept
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# needs >= head_min_positives labelled images before a head is trained;
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# head_auto_apply_precision is the precision bar a head must clear (at some
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# operating point) to "graduate" into earned auto-apply. Operator-tunable.
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head_min_positives: Mapped[int] = mapped_column(
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Integer, nullable=False, default=8
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)
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head_auto_apply_precision: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.97
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)
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# Earned auto-apply (#114). A graduated head fires (tags images without a
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# human) when this master switch is on AND the head has at least
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# head_auto_apply_min_positives clean labels — so a precise-looking but
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# under-supported low-N head can't spray tags across the library. ON by
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# default (operator-asked 2026-06-29: opt-OUT, not opt-in); the support +
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# measured-precision gates keep it safe, and every auto-tag is reversible.
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head_auto_apply_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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head_auto_apply_min_positives: Mapped[int] = mapped_column(
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# Support floor raised 30→50 (operator-asked 2026-07-06): a head needs
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# more human labels before it may fire without a human.
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Integer, nullable=False, default=50
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)
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# CCIP character-match cosine cut (#114). 0.85 default — the v1 flat 0.75
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# over-fired (high-reference characters matched a scatter of images); 0.85
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# keeps the confident single-character matches. Tunable from the agent card.
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ccip_match_threshold: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.85
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)
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# CCIP auto-apply (#114). Confident matches (>= ccip_auto_apply_threshold,
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# above the suggest cut) auto-tag on a daily sweep. ON by default (opt-out);
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# single-character references + the high bar keep it safe, every tag reversible.
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ccip_auto_apply_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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ccip_auto_apply_threshold: Mapped[float] = mapped_column(
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# Raised 0.92→0.95 (operator-asked 2026-07-06) so only very confident
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# character matches auto-tag.
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Float, nullable=False, default=0.95
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)
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# Default = SigLIP 2 (so400m, 512px) for new installs (migration 0069);
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# existing libraries keep their stored value until the operator re-embeds.
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embedder_model_version: Mapped[str] = mapped_column(
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String(128), nullable=False, default="siglip2-so400m-patch16-512"
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)
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# The HF model NAME the embedder loads (server CPU embed + announced to the
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# GPU agent in the lease). Operator-settable so the embedder is a choice, not
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# a hardcode (#1190): set name + version together, then re-embed + retrain.
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embedder_model_name: Mapped[str] = mapped_column(
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String(128), nullable=False, default="google/siglip2-so400m-patch16-512"
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)
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# -- Crop proposers / detectors (#1202, #134) --------------------------
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# WHERE-to-crop YOLO detectors feeding the crop→SigLIP bag + CCIP. Config
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# lives HERE (DB) and is announced to the GPU agent in the lease — same as
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# the embedder model — so it is UI-tunable with NO restart, and the agent's
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# env is bootstrap-only. Each weights spec is an ultralytics builtin name,
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# an http(s) URL, or "hf_repo::file" (agent's _resolve). enabled off (or an
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# empty weights) skips that proposer. All ON by default (operator 2026-07-05)
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# so a fresh install crops out-of-the-box.
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# person: general COCO figure detector for Western/realistic art the anime
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# person-detector misses → NMS-merged with imgutils → CCIP + concept.
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detector_person_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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detector_person_weights: Mapped[str] = mapped_column(
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String(512), nullable=False, default="yolo11n.pt"
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)
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detector_person_conf: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.35
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)
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# anatomy: booru_yolo anime/furry/NSFW torso components → concept crops.
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# Default = yolov11m_aa22 (26 classes, best mAP50-95 0.96), committed in the
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# upstream repo so the URL resolves. License UNSTATED — fine for a private
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# homelab (operator accepted #1202).
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detector_anatomy_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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detector_anatomy_weights: Mapped[str] = mapped_column(
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String(512), nullable=False,
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default=(
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"https://github.com/aperveyev/booru_yolo/raw/main/models/"
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"yolov11m_aa22.pt"
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),
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)
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detector_anatomy_conf: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.30
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)
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# panel: comic page → panel regions → concept crops (Apache-2.0, YOLOv12x).
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detector_panel_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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detector_panel_weights: Mapped[str] = mapped_column(
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String(512), nullable=False,
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default="mosesb/best-comic-panel-detection::best.pt",
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)
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detector_panel_conf: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.30
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)
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# Per-frame caps bound the crop→embed explosion; max_regions is the hard
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# per-job backstop; dedupe_iou drops near-duplicate crops before the embed.
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detector_max_figures: Mapped[int] = mapped_column(
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Integer, nullable=False, default=8
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)
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detector_max_components: Mapped[int] = mapped_column(
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Integer, nullable=False, default=8
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)
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detector_max_panels: Mapped[int] = mapped_column(
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Integer, nullable=False, default=8
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)
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detector_max_regions: Mapped[int] = mapped_column(
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Integer, nullable=False, default=128
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)
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detector_dedupe_iou: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.85
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)
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# -- CCIP character prototypes (#1317) ---------------------------------
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# The per-character reference set is precomputed + refreshed INCREMENTALLY
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# (services.ml.character_prototypes) instead of rebuilt on the request path.
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# ccip_ref_signature is the cheap GLOBAL gate — when it's unchanged the
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# refresh no-ops; ccip_prototype_cap bounds the reference vectors kept per
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# character so MATCH cost doesn't grow with a character's popularity.
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ccip_ref_signature: Mapped[str | None] = mapped_column(
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String(128), nullable=True
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)
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ccip_prototype_cap: Mapped[int] = mapped_column(
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Integer, nullable=False, default=64
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, server_default=func.now()
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)
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