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>
Read cutover verified in prod (suggestions + allowlist read image_prediction;
backfill complete at 908k rows / 51k images). Removes the old JSON column and
everything that fed it:
- ImageRecord.tagger_predictions column removed; migration 0046 DROPs it.
tagger_model_version kept as the "tagged / current?" signal the backfill
sweep reads (needs-tagging check switched to tagger_model_version IS NULL).
- tag_and_embed no longer dual-writes the JSON — image_prediction is the only
write path.
- importer re-import reset drops the JSON line (image_prediction rows are
already deleted on re-import).
- Retired the one-time #768 backfill task + the #764 prune task, their admin
endpoints, and their Maintenance cards (Backfill/PrunePredictionsCard).
- Tests seed/assert via image_prediction; stale column refs removed.
Disk reclaim is NOT automatic: DROP COLUMN is a catalog change. Run
`VACUUM FULL image_record` off-hours afterward to return the ~100 GB to the OS
so DB backups go small (#739). image_prediction (~90 MB) stays in pg_dump — it's
the source of truth now.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Switch every prediction READER off the JSON column onto the normalized
image_prediction table. Parity by construction: each reader loads the same
{raw_name: {category, confidence}} dict it consumed before (via small
_load_predictions helpers), so all downstream threshold/alias/merge/consensus
logic is byte-identical — only the data source changed.
- suggestions.SuggestionService.for_image (and for_selection via it)
- ml.apply_allowlist_tags (iterates images that have prediction rows)
- importer re-import reset deletes the image's prediction rows
The tagger_predictions JSON column is still dual-written (step 1) so it stays
valid during transition; the backfill task's NULL check still works. Removing
the JSON write + DROP column + retiring the #764 prune is the cleanup
follow-up (needs a quiesced-worker window for the DROP lock).
Tests: shared tests/_prediction_helpers.seed_predictions seeds the table;
read-path tests (suggestions, bulk consensus, allowlist apply, API) seed there
instead of ImageRecord.tagger_predictions.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
apply_allowlist_tags: 4 modes (tag-only / image-only / both / full sweep),
matches a tag to a prediction either by direct name or via alias
(name, category) resolution, gates on per-tag min_confidence, skips
applied/rejected, applies source='ml_auto'. recompute_centroid /
recompute_centroids: async-bridged calls into CentroidService, delta-gated.
Beat: daily backfill, daily centroid recompute, daily allowlist sweep.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
tag_and_embed: Camie + SigLIP on one image (video → 10-frame sample,
max-pool tags, mean-pool embeddings), stores predictions/embedding with
model versions, then enqueues per-image allowlist apply. backfill:
keyset-paginated discovery of images missing predictions/embeddings for
the current model versions (restart-safe). apply_allowlist_tags stub
included so .delay() resolves between commits (filled in Task 9).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen