* refactor: rename CACHE_DIR to DATA_DIR, default path .if_cache → data
CACHE_DIR held both the embedding cache and the SQLite tracker DB, making
the name misleading. DATA_DIR is more accurate.
- Config reads DATA_DIR first; falls back to CACHE_DIR with a deprecation
warning so existing setups don't break on upgrade
- Default local path: data (was .if_cache)
- Docker default path: /app/data (was /app/.if_cache)
- Internal references (embeddings.py, upload_tracker.py) updated to DATA_DIR
- compose.yml, .env.example, README, wiki, and changelog updated
- Version bumped to 0.5.1
* chore: update lockfile
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* refactor: rename CACHE_DIR to DATA_DIR, default path .if_cache → data
CACHE_DIR held both the embedding cache and the SQLite tracker DB, making
the name misleading. DATA_DIR is more accurate.
- Config reads DATA_DIR first; falls back to CACHE_DIR with a deprecation
warning so existing setups don't break on upgrade
- Default local path: data (was .if_cache)
- Docker default path: /app/data (was /app/.if_cache)
- Internal references (embeddings.py, upload_tracker.py) updated to DATA_DIR
- compose.yml, .env.example, README, wiki, and changelog updated
- Version bumped to 0.5.1
* chore: update lockfile
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
- release.yml: replace per-variant lockfile generation loop with a
single 'uv lock'; add idempotent release creation (skip if tag
already has a release so re-triggered runs don't 422)
- docker-publish.yml: remove stale uv-cpu/rocm/intel.lock entries
from paths-ignore (those files no longer exist)
- README: CACHE_DIR description now names winnow_tracker.db; tracker
description mentions SQLite
Two fixes found during post-refactor audit:
1. upload_tracker: remove COUNT(*) guard from _maybe_migrate. The guard
blocked re-migration when a previous run successfully committed both
JSON files but a PermissionError on the second rename() left it on
disk. On the next startup COUNT > 0 → early return → rejected IDs
permanently unimported. INSERT OR IGNORE is idempotent so re-running
migration is always safe; guard not needed.
Also wrap each rename() in its own try/except so a failure on one
file is logged and does not propagate uncaught.
2. reconcile: break early when new_count > target is detected in the
poll loop. Previously the loop ran all four delay intervals (1+2+4+8s)
before the post-loop > target branch fired, wasting up to 15 seconds
when a concurrent external upload was visible on the first poll.
- upload_tracker: partial migration now rolls back atomically on failure;
JSON renamed only after successful commit so failed runs retry cleanly
- upload_tracker: allowlist score_col in _pick_mapped_file to close
latent SQL injection surface
- config: move load_dotenv() from module import into _load() so no I/O
at import time and reset() fully resets env loading
- config: use is None checks for IMMICH_URL/OUTPUT_DIR config-file
fallback so explicitly empty env vars are not overridden by the file
- executor: skip reconcile when Frigate API is unreachable at upload
start — tracker baseline is incomplete and would mis-trigger the
external-upload guard, permanently losing file mappings
- reconcile: change polling break condition from >= to == target so
transient overshoots don't prematurely exit the loop and trigger
the external-upload guard
- jobs: apply capacity cap as the selection limit rather than truncating
post-selection by position, so the diversity algorithm works within
the right budget from the start
- Dockerfile: explicit gpu branch + exit 1 on unknown VARIANT instead
of silent fallback
_build_job no longer calls filter_already_uploaded internally; callers pass
pre-filtered assets so there's no double DB hit and the interactive path
restores the original prompt order (retry_rejected asked before strategy,
so post-dedup count informs the choice). Skip-count rprint restored in
auto_configure. Late 'import time' inside reconcile_frigate_mappings moved
to module level.
- rocm/intel/gpu extras are x86_64-only; aarch64 wheels don't exist so uv
failed to resolve them when required-environments includes aarch64
- test.yml: switch from --all-extras (broken by conflicts + missing wheels)
to --extra cpu which is cross-platform and sufficient for unit tests
- update-lockfile.yml: drop old file-swap loop; single pyproject means a
single uv lock run and a single uv.lock to commit
- Config: remove _ConfigAccessor and ConfigManager; use __getattr__ for lazy
loading on single _Config class; re-register self as _instance in __getattr__
so reset() always clears the correct object (item 1)
- upload_tracker: replace hand-rolled JSON store with sqlite3; auto-migrates
existing JSON on first run; remove dead record_frigate_file function;
connection re-opens when CACHE_DIR changes for test isolation (items 2, 8)
- diversity: move ThreadPoolExecutor import to module level; inject optional
fetch_fn parameter for testability (items 3, 6)
- pyproject: consolidate 4 variant files into extras (gpu/rocm/intel/cpu);
update Dockerfile to use --extra flag; delete variant pyproject/lock files;
uv.lock needs regen with `uv lock` after this change (item 4)
- jobs: extract _build_job helper to separate business logic from terminal I/O;
auto_configure delegates dedup/selection to _build_job (item 5)
- logging: convert f-string log calls to % interpolation throughout all winnow/
modules (item 7)
- reconcile: new module with reconcile_frigate_mappings and
enrich_asset_with_face_data extracted from executor.py (item 9)
- scheduler: print next scheduled run time after startup and after each run;
fix f-string logger.error call (item 10)
22.04 non-GPU amd64 bases were never updated when arm64 moved to 24.04.
Python 3.13 is still pulled from deadsnakes PPA (26.04 ships 3.14 natively).
intel stays on 22.04: the Intel GPU repo URL is pinned to the "jammy"
codename and cannot be bumped until Intel publishes 26.04 packages.
cache.py — model fingerprint auto-invalidation:
Replace hardcoded "buffalo_l_v1" version string with a fingerprint
derived from buffalo_l .onnx file sizes and mtimes. EmbeddingCache now
computes this at init time; stale embeddings from replaced or updated
model files are automatically invalidated. Falls back to the static
string before the model is downloaded.
Note: existing caches built against the old key will miss on the first
run after upgrade and recompute cleanly.
frigate_api.py — Frigate version check:
Add get_frigate_version() (GET /api/version). Called at the start of
upload_to_frigate(); warns if below v0.16 where the face training API
endpoints don't exist.
immich_api.py + cli.py — Immich version check:
Add get_immich_version() (GET /api/server/version). Called at startup
before get_people(); warns if below v1.106 where the face data and
merge APIs winnow depends on aren't guaranteed present.
Remaining TODO(frigate-api) annotations are left in place — they require
Frigate to expose per-file embeddings or a rebuild-complete signal before
they can be addressed.
Adds inline LIMITATION / TODO(frigate-api) comments at each specific
code site rather than a separate doc that would drift from the code.
frigate_api.py — recognize_face:
Mean-embedding limitation: score reflects the arithmetic mean of all
training embeddings. A bimodal set (frontals + profiles) has a mean
between clusters, making both ends look more novel than they are.
Fixable if Frigate exposes per-file embeddings for nearest-neighbour
comparison.
frigate_api.py — get_all_frigate_person_files:
"train" key exclusion is a hardcoded string. If Frigate adds other
special top-level keys in /api/faces they'll be silently treated as
person names. Needs a typed schema when Frigate documents the contract.
executor.py — recognize_face call site:
Async rebuild: each deletion triggers a background model rebuild in
Frigate. Subsequent recognize calls in the same run return None
(rebuild in progress), degrading quality replacement for later
candidates. Fixable with a rebuild-complete signal from Frigate.
executor.py — effective_count / manual file handling:
Manually-added files are invisible to diversity decisions. Winnow
observes their effect only indirectly via the Frigate score, not by
measuring their embedding distribution. Per-file embeddings from
Frigate would allow direct diversity measurement against the full set.
executor.py — Frigate version assumption:
All face training endpoints are v0.16+. No version check at startup;
failures on older versions are opaque 404s.
cache.py — MODEL_VERSIONS:
Version string is a hardcoded constant. Manual model file replacement
(custom weights, InsightFace update) won't invalidate cached embeddings.
Needs file-checksum-derived versioning or a CLEAR_EMBEDDING_CACHE flag.
diversity.py — thumbnail-resolution embeddings:
Diversity selection runs InsightFace on preview thumbnails; the actual
training crop comes from full-resolution originals. Negligible in
practice but degrades if Immich preview quality is low.
Tests cover the core ML pipeline algorithms in diversity.py — previously
untested. No network or model dependencies; all pure-function or
numpy-only paths:
- Face bbox and confidence extraction from Immich metadata, including
person_id filtering and missing-data edge cases
- Face crop scaling: verifies bbox coordinates are correctly scaled when
the thumbnail dimensions differ from the metadata image dimensions
- Near-duplicate dedup: removal below cosine threshold, quality-score
preference between duplicates, zero-quality-score treated as zero not
missing (falsy bug guard)
- K-Medoids: correct medoid count, distinctness, valid index range, and
full-N edge case
- Adaptive threshold: positive output, floor at 0.05 for identical
embeddings, single-point, scales with embedding spread
- Time-spread fallback: exact count, all-under-limit passthrough,
auto→30 default, first/last inclusion
- Cluster-aware selection: exact limit, subset invariant, auto-stop on
tight cluster, hard-example confidence weighting accepted
Remove torch/torchvision/transformers/ultralytics from all three variant
pyproject files (rocm/cpu/intel) — no longer needed since SigLIP and YOLO
were removed in v0.4.10. Update version to 0.4.10 and description.
NOTE: uv-rocm.lock, uv-cpu.lock, uv-intel.lock are now stale and must be
regenerated in their respective platform environments before the next Docker
build of those variants.
Also fix fetch_face_data() docstring to not mention embedding (removed field).
- immich_api.py: remove FaceData.embedding field and numpy import — Immich face
embeddings were never consumed after being fetched; bbox/confidence is all that's used
- embeddings.py: remove immich_embedding param from get_embedding() — never passed by
any caller; simplify docstring accordingly
- image_processing.py: wrap insightface_app.get() in warnings filter to suppress the
scikit-image FutureWarning about estimate being deprecated (already suppressed in
embeddings.py for the diversity path, was leaking from the crop-alignment path)
- README.md: remove two stale "object mode" / "object classification" fragments
After removing the object pipeline, several dead 'mode' artifacts remained:
- executor.py: unpack `config` from job even though it was no longer read
- jobs.py: set `"mode": "face"` in both configure paths (key never consumed)
- compose.yml: TRAINING_MODE=face env, OBJECT_CLASS comment, HF_HOME, stale MAX_AUTO_IMAGES default note
- Dockerfile: "and object classification" label, /models/huggingface mkdir, HF_HOME ENV
- cache.py: drop siglip MODEL_VERSIONS entry
- log_config.py: drop ultralytics/transformers/torch from noise silencers
- scheduler.py: drop HF_HOME and HuggingFace model check
- scripts/benchmark.py: drop bench_siglip and make_random_image
winnow is a face recognition training tool. Object mode required manual
file placement with no Frigate API, pulled in torch/torchvision/transformers/
ultralytics (~2 GB), and was architecturally misaligned with the project goal.
Removed:
- process_object_mode (YOLO inference), get_yolo_model
- SigLIP model stack (get_siglip_model, get_object_embedding, batch variant)
- entity_type branching throughout diversity, embeddings, jobs, executor
- TRAINING_MODE and OBJECT_CLASS env vars
- torch, torchvision, transformers, ultralytics dependencies
- pytorch index entries from pyproject.toml
- Object mode from README (Modes section, env var table, How It Works)
- How It Works step 9: "upload freely" → note novelty gate may skip below-cap candidates
- Persistence note: "Frigate rejections" → "rejected assets" (covers confidence skips too)
- RETRY_REJECTED description: explicitly covers all rejection types, not just Frigate
- Image Quality section: split into user-adjustable controls and calibrated image
processing defaults with a support disclaimer to deter blind tuning
Previously, assets that passed embedding-phase selection but failed the
faces API confidence check in execute_jobs were silently skipped with no
tracker entry. They appeared as valid candidates on every future run,
were re-selected, and re-skipped in an endless cycle. Now they are
marked rejected so they are excluded from future runs. RETRY_REJECTED=true
clears them if Immich later re-processes the image.
FRIGATE_SCORE_CEILING now defaults to dynamic mode (unset): below-cap
candidates are skipped if their pre-upload Frigate score exceeds the
most-redundant tracked file's score. This catches conditions already
covered by manually-added Frigate images that winnow cannot track —
the embedding-based diversity selection has no visibility into those.
Set FRIGATE_SCORE_CEILING=0 to disable; a positive value (e.g. 0.85)
still acts as a fixed hard ceiling. First-run safety is unchanged
(pre_run_count==0 prevents recognize_face from being called).
The two quality replacement branches (Frigate-score and blur-score)
shared identical structure and are merged into a single code path
parameterised by score source and comparison direction.
Also raises MIN_FACE_COUNT default from 0 to 3 and updates the
config test to match.
AUTO_MODE (batch/unattended) and STRATEGY=auto (diversity algorithm) shared
the same word for unrelated concepts. Renaming the strategy value to
'adaptive' eliminates the ambiguity. The old value is kept as a silent alias
so existing configs continue to work.
Interactive menu updated from "Auto (Objective Diversity)" to "Adaptive
Diversity". README AUTO_MODE description clarified to emphasise unattended
batch processing, not selection strategy.
- `:latest` image tag listed CUDA 13.3 — actual base is 12.8.1
- MERGE_DUPLICATE_PEOPLE existed in config but was absent from env var table
- TRACE_CROP_SIZE existed in CLI but was absent from env var table
- RESET_PERSON description now mentions `*` wildcard for bulk reset of all people
- Step 5 (near-duplicate removal) was added in v0.4.4 but never
documented; added between embedding and diversity selection
- Auto-stop was described as 'stops when similarity exceeds threshold'
which is backwards; it stops when the next candidate's distance to
already-selected images falls below the threshold (too similar, not
enough new information)
- Renumbered steps 5-8 to 6-9
- Tightened hard-example weighting description to match the code
All _load() calls after the first return the cached dict instead of
re-reading disk. _save() updates both disk and cache atomically.
Drops per-person tracker reads from ~90 to ~1 in the upload loop.
Keyed by resolved file path so test isolation (unique tmp_path dirs)
is preserved with no fixture changes needed.
- _dedup_embeddings: pre-allocated (Q,D) buffer replaces vstack-on-keep,
dropping O(K²×D) copy overhead down to O(K×D) fill work
- _kmedoids: swap cost sum replaced with numpy fancy-index reduction,
~20-50x faster per swap evaluation
- _reconcile_frigate_mappings: O(L) load/save pairs collapsed to one
batch write via record_frigate_files_batch
Fetching up to MAX_PAGES*page_size (1M) assets before the 3000-item
diversity pool cap was applied could exhaust memory on large Immich
libraries. Early-exit once 5000 items are collected — the pool cap
of 3000 makes anything beyond that wasteful. Also filter null/non-dict
items from page responses at fetch time.
- _dedup_embeddings: rebuild kept_stack only on keep (was every iteration → O(N²))
- _dedup_embeddings: fix quality_score sort key to use explicit None check (falsy-zero)
- _select_by_embedding: add post-dedup pool < limit guard with warning
- executor: use full resp.text for 'face' keyword check; only truncate display snippet
- _safe_person_dir: avoid false "//" prefix when output_dir resolves to filesystem root
0.10 only removed burst shots (distance 0.01-0.05). Same-event photos with
similar pose and lighting sit at 0.10-0.20 and were passing through,
producing visually similar training images especially for people with small
datasets. 0.20 removes these while still preserving genuinely different
poses, expressions, and lighting conditions.
os.path.join silently discards the base when the second arg is absolute,
and '../..' sequences escape the output tree. _safe_person_dir() resolves
both paths with realpath and rejects any name that lands outside the output
directory, logging an error and skipping the job rather than touching an
unintended path.
- RESET_PERSON=* resets every tracked person (deletes their Frigate files
and clears the tracker). Any other value resets that specific person by
name, including someone literally named 'all'.
- Near-duplicate removal pass added before diversity clustering: greedily
drops candidates within 0.10 cosine distance of a higher-quality image,
eliminating burst-shot duplicates that FPS would otherwise pass through.
Burst shots produce embeddings that differ slightly (~0.01-0.05 cosine
distance) due to JPEG noise and minor lighting variation, so FPS does not
filter them. Add a greedy dedup pass after embedding collection: sort
candidates by quality score descending, then drop any candidate within
0.10 cosine distance of an already-kept image. The best frame from each
near-identical group survives; the rest are dropped before clustering.
- HTTP 500 errors from Frigate no longer echo the response body to the user
(Frigate's generic message says 'Try restarting Frigate' which is wrong —
500s on upload are almost always image-specific, not a health issue). The
detail is now logged at debug level. HTTP 400 detail is still shown since
'No face was detected' is genuinely useful.
- np.median on empty upper triangle (n=1 after quality filtering) no longer
emits RuntimeWarning; _compute_adaptive_threshold returns the floor (0.05)
immediately when there are no pairwise distances to sample.
- k-medoids cluster count floor raised to 1 (was 0 when n < 3), preventing
k=0 being passed to _kmedoids.
When Immich has multiple person records sharing a name (e.g. unmerged
face clusters), winnow would previously run separate jobs for each,
with the second job wiping the first job's output directory — resulting
in far fewer training images than expected.
New behaviour:
- At startup, duplicate names are detected and a warning is printed
showing asset counts for each duplicate.
- By default (MERGE_DUPLICATE_PEOPLE=false), only the person with the
most assets is processed; smaller duplicates are skipped cleanly.
- With MERGE_DUPLICATE_PEOPLE=true, the duplicates are permanently
merged inside Immich via PUT /api/people/{id}/merge (keeps the
largest), then the people list is re-fetched before jobs run.
Also adds an explicit comment in executor.py confirming that replacement
targets come exclusively from tracker-mapped files, so manually-added
Frigate training images are never selected for deletion.
Immich's /api/faces endpoint only returns bounding boxes, not facial
landmarks. This meant align_face() never fired and all crops fell back
to a plain bbox rectangle — producing partial crops (forehead-only,
off-angle faces) when Immich's detection was slightly off.
Now, when ENABLE_FACE_ALIGNMENT is true and InsightFace is loaded,
execute_jobs() passes the app to process_face_mode(). For each face,
it expands the Immich bbox by 50%, crops that search region, runs
InsightFace detection within it, and aligns the nearest face to the
standard ArcFace 112×112 format using norm_crop(). Falls back to bbox
crop if InsightFace finds no face in the search region.
The InsightFace model is already in GPU memory from the diversity/
embedding phase, so the singleton lookup adds no load cost.
CUDA 13.3 requires driver >= 575 but the host only has 570 (error 804).
CUDA 12.8.1 is the highest version supported by driver 570 and works
correctly with the NVIDIA Container Toolkit.
Add scripts/benchmark.py to measure InsightFace + SigLIP latency and
throughput across GPU and CPU modes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
release.yml now calls docker-publish.yml via workflow_call instead of
re-running all four image builds independently. docker-publish.yml gains
workflow_call inputs (tag, version) for release context; branch trigger
is narrowed to dev only (main changes only land via tagged releases).
Each release previously built all four variants twice (~90 min) — once on
merge to main, once on tag push. Now it builds once.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
CUDA 13.3 requires driver >= 575 but the host only has 570 (error 804).
CUDA 12.8.1 is the highest version supported by driver 570 and works
correctly with the NVIDIA Container Toolkit.
Add scripts/benchmark.py to measure InsightFace + SigLIP latency and
throughput across GPU and CPU modes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
release.yml now calls docker-publish.yml via workflow_call instead of
re-running all four image builds independently. docker-publish.yml gains
workflow_call inputs (tag, version) for release context; branch trigger
is narrowed to dev only (main changes only land via tagged releases).
Each release previously built all four variants twice (~90 min) — once on
merge to main, once on tag push. Now it builds once.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously reset_person wiped the local tracker but left existing Frigate
training files as orphans, causing the next run to upload a full new batch
on top of them. Now deletes all winnow-managed files from Frigate first so
the next run starts truly clean. Manually-added Frigate files are never
touched.
Also fixes a spurious warning when FRIGATE_URL is unset: the deletion step
is now skipped at info level rather than logging a misleading error. Moves
the deferred import to top-level and eliminates a double disk read.
Bumps to 0.4.1. Also fixes ruff lint violations in executor.py (import
sort, line length) and promotes the "winnow only touches files it uploaded"
callout to the README intro.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- upload_tracker: extract _pick_mapped_file() private helper; get_lowest_quality_mapped_file
and get_most_redundant_mapped_file are now one-liners over the same body
- upload_tracker: remove_frigate_file now also prunes the corresponding frigate_scores entry,
preventing unbounded accumulation of orphaned score entries across replacement cycles
- frigate_api: get_frigate_face_counts delegates to get_all_frigate_person_files, eliminating
the duplicated "name != 'train' and isinstance(files, list)" filter body
- executor: cache has_frigate_scores(name) as person_has_fscores before the per-file loop;
refresh it after each remove_frigate_file call and after each scored upload, eliminating
two redundant disk reads per at-cap file iteration
- executor: casefold() both sides of the recognize_face person-name comparison so a Frigate
casing normalization or manual-registration casing mismatch does not silently suppress scoring
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Frigate pre-upload scoring, quality replacement inversion, bootstrap fix,
FRIGATE_SCORE_CEILING / ENABLE_FRIGATE_SCORES, removal of post-upload gate.
See CHANGELOG.md [0.4.0] for the full list.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Finalizes the 0.4.0 release:
- Version bumped to 0.4.0 in pyproject.toml
- CHANGELOG.md: add [0.4.0] section covering Frigate pre-upload scoring,
quality replacement inversion, bootstrap fix, FRIGATE_SCORE_CEILING,
ENABLE_FRIGATE_SCORES, removal of post-upload quality gate, and all
doc/default corrections
- README.md: step 8 updated for dual-mode replacement, FRIGATE_SCORE_CEILING
and ENABLE_FRIGATE_SCORES added to env var table, MIN_FACE_WIDTH and
BLUR_THRESHOLD defaults corrected (50→90, 100→120)
- .env.example: FRIGATE_SCORE_THRESHOLD replaced with FRIGATE_SCORE_CEILING;
QUALITY_REPLACEMENT line added; comments updated to match current semantics
- winnow/executor.py: bootstrap fix — recognize now called for all below-cap
uploads when ENABLE_FRIGATE_SCORES=true (was gated on CEILING > 0)
- winnow/upload_tracker.py: frigate_scores schema comment corrected to
pre-upload; get_most_redundant_mapped_file() added
- winnow/frigate_api.py: recognize_face returns (face_name, score)|None tuple
so wrong-person scores never drive replacement or ceiling decisions
- winnow/config.py: FRIGATE_SCORE_THRESHOLD renamed to FRIGATE_SCORE_CEILING;
ENABLE_FRIGATE_SCORES added
- tests/test_upload_tracker.py: 4 new tests for get_most_redundant_mapped_file
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
flan
