bvandeusen
369e3de684
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>
FabledCurator
Self-hosted media curation — gallery, ML tagging, and subscription-driven downloading in one app. Part of the FabledSword family.
Combines what was ImageRepo (gallery, ML, importer) and GallerySubscriber (gallery-dl wrapper, subscriptions, credential capture) into a single product.
Status
Pre-v1. Not yet functional.
Quick start
For local development and testing, just:
docker compose up -d
# UI: http://localhost:8080
That uses sane dev defaults baked into docker-compose.yml and the dev
override (docker-compose.override.yml, auto-merged) — local builds, DEBUG
logging, exposed Postgres + Redis ports on the host. No .env required.
For a production-like deployment, override the dev defaults via shell env
or a .env file (see .env.example for the variable names) and use:
docker compose -f docker-compose.yml up -d
# (skips the override so containers pull registry images)
Deployment posture
FabledCurator is designed to run inside a self-hosted homelab environment over plain HTTP. If you want TLS, terminate it at your reverse proxy. The app does not generate certificates, redirect to HTTPS, or set HSTS.
CI / Forgejo setup
The repo's workflows expect:
-
Runner label
python-ci— a Forgejo runner with Python 3.14, ruff, and Node 22 pre-installed. Bothci.ymlandbuild.ymluse this label. The runner image (runner-base:python-ci) is built fromCI-Runner/CI-python/in the operator's workspace;make pushfrom that directory builds and pushes a new image when toolchain pins change. -
Repo secret
RELEASE_TOKEN— a Forgejo PAT with the following scopes:write:package+read:package— fordocker pushtogit.fabledsword.comwrite:release— for future release-cutting workflowswrite:issue— for future issue-management automation
Generate at https://git.fabledsword.com/user/settings/applications. The injected
GITHUB_TOKENcannot be used because it lackswrite:package.
License
Personal project; use at your own discretion.