Video tag noise root cause: frames were a FIXED count (6) max-pooled — a tag
firing on one frame survived at peak confidence, and a fixed count under-samples
long multi-scene videos so real scene-local tags looked like noise.
Redesign (operator-steered):
- Sample at a fixed CADENCE — one frame every `video_frame_interval_seconds`
(default 4) across the 5–95% window — so a tag's frame-presence reflects real
screen time independent of video length. Capped at `video_max_frames` (default
64): a long video stretches the spacing instead of exploding into hundreds of
inferences, bounding per-video cost on the single ml-worker (per-frame ffmpeg
timeout also cut 60s→30s).
- Aggregate with `_aggregate_video_predictions`: keep a tag only if it appears in
>= `video_min_tag_frames` sampled frames (≈ that many × interval seconds on
screen — duration-independent noise rejection), with confidence = MEAN over the
frames it appears in (not max). Clamps the threshold to the sample count so a
1–2-frame short video still tags.
- All three knobs are DB-backed ml_settings (migration 0053), patchable via
/api/ml/settings + sliders in the ML settings card — replaces the
VIDEO_ML_FRAMES env var (product-not-project).
Tests: aggregation drops one-frame noise + means corroborated tags + clamps on
short videos; settings round-trip + min>max validation. Replaced the
_maxpool_predictions unit test.
NOTE: this is the QUALITY half of #747. The perf half — the ml-worker runs
CPU-only — is GPU enablement, tracked separately in #872.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
bvandeusen
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