Merge branch 'dev'

Documents object mode removal cleanup, dead embedding path removal,
FutureWarning fix, variant pyproject sync, and docs/wiki updates.

CHANGELOG.md

@@ -7,6 +7,47 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.4.11] - 2026-06-14
+
+### Removed
+
+- **Object mode pipeline fully removed**: YOLO object detection, SigLIP image classification, `TRAINING_MODE`, and `OBJECT_CLASS` env vars are gone. Frigate has no training API for objects; the ~2 GB model stack (torch, torchvision, transformers, ultralytics) was dead weight.
+- **Dead Immich embedding path removed**: `FaceData.embedding` field and the `immich_embedding` parameter to `get_embedding()` were never consumed by any caller. Both removed along with the NumPy import in `immich_api.py` that existed solely for that path.
+- **Dead `mode` config key removed**: `"mode": "face"` was written into job config dicts in `jobs.py` but never read after object mode removal.
+
+### Fixed
+
+- **InsightFace `FutureWarning` suppressed in crop-alignment path**: the `insightface_app.get()` call in `image_processing.py` now wraps the same `warnings.catch_warnings()` suppressor already present in `embeddings.py`, preventing scikit-image deprecation noise in logs.
+
+### Changed
+
+- **Variant pyproject files synced to current state**: `pyproject-rocm.toml`, `pyproject-cpu.toml`, `pyproject-intel.toml` were at v0.2.13 and still listed torch/transformers/ultralytics. Updated to v0.4.11 and cleaned to face-only deps. Note: corresponding lockfiles (uv-rocm.lock, uv-cpu.lock, uv-intel.lock) need regeneration in their respective platform environments.
+- **`MERGE_DUPLICATE_PEOPLE` documented**: README and wiki now explain the default warn-and-skip behaviour vs. setting `true` for a permanent Immich merge, with irreversibility callout.
+- **Wiki fully updated**: all five wiki pages rewritten to remove object mode references, correct model size (~300 MB InsightFace vs former ~1–2 GB HuggingFace+InsightFace), fix default values (`MAX_AUTO_IMAGES` 80→20, `MIN_FACE_COUNT` 0→3), add `MERGE_DUPLICATE_PEOPLE` coverage, and update GPU verification commands for current ONNX provider API.
+
+## [0.4.10] - 2026-06-14
+
+### Changed
+
+- **`MAX_AUTO_IMAGES` default lowered from `80` to `20`**: winnow is designed to fill the gap where manual Frigate training images don't exist — not to be the primary dataset. A conservative default ensures winnow-imported images remain secondary to hand-picked ones where both exist.
+
+## [0.4.9] - 2026-06-14
+
+### Changed
+
+- **`FRIGATE_SCORE_CEILING` is now dynamic by default**: previously defaulted to `0` (disabled). Now unset (default) enables a self-calibrating novelty gate — 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. Set `FRIGATE_SCORE_CEILING=0` to disable entirely; set a positive value (e.g. `0.85`) for a fixed hard ceiling.
+- **Quality replacement branches consolidated**: the Frigate-score and blur-score replacement paths in the upload loop shared identical structure. Merged into a single code path parameterised by score source and comparison direction.
+- **`MIN_FACE_COUNT` default raised from `0` to `3`**: people with fewer than 3 tagged photos produce degenerate training sets; skipping them by default avoids noisy runs.
+- **`STRATEGY=adaptive`** is the new primary name for embedding-based diversity selection; `auto` remains a silent alias for backwards compatibility.
+- **`MERGE_DUPLICATE_PEOPLE` and `TRACE_CROP_SIZE`** added to the README env var table (were in the codebase but undocumented).
+- **CUDA version corrected** in the image tags table (was 13.3, actual base image is 12.8.1).
+
+## [0.4.8] - 2026-06-14
+
+### Changed
+
+- **Tracker write-through cache**: `upload_tracker` now keeps an in-memory copy of each JSON file keyed by its resolved path. All reads after the first hit the cache instead of disk; writes go to both disk and cache atomically. Cuts per-person disk I/O in the upload loop from ~90 reads to ~1, with no API or behaviour changes.
+
 ## [0.4.7] - 2026-06-14
 
 ### Changed

Dockerfile

@@ -67,7 +67,7 @@ FROM base-${TARGETARCH}-${VARIANT} AS runtime
 ARG VARIANT=gpu
 ARG VERSION=dev
 LABEL org.opencontainers.image.title="winnow" \
-      org.opencontainers.image.description="Selects diverse, high-quality photos from Immich as training data for Frigate face recognition and object classification." \
+      org.opencontainers.image.description="Selects diverse, high-quality photos from Immich as training data for Frigate face recognition." \
       org.opencontainers.image.source="https://github.com/sudolulo/winnow" \
       org.opencontainers.image.licenses="AGPL-3.0-or-later" \
       org.opencontainers.image.version="${VERSION}"
@@ -116,7 +116,7 @@ https://repositories.intel.com/graphics/ubuntu jammy flex" \
     fi
 
 RUN groupadd -g 568 apps && useradd -u 568 -g apps -m -s /bin/bash appuser \
-    && mkdir -p /models/.insightface /models/huggingface \
+    && mkdir -p /models/.insightface \
     && chown -R appuser:apps /app /models
 
 WORKDIR /app
@@ -124,7 +124,7 @@ USER appuser
 # PYTHONPATH=/app makes the winnow package importable from the entry point script.
 # uv sync builds the wheel before winnow/ is COPY'd, so site-packages has only
 # the dist-info. Explicitly adding /app lets Python find winnow/__init__.py there.
-ENV HF_HOME=/models/huggingface INSIGHTFACE_HOME=/models/.insightface PYTHONPATH=/app
+ENV INSIGHTFACE_HOME=/models/.insightface PYTHONPATH=/app
 
 HEALTHCHECK CMD test -f /app/entrypoint.sh || exit 1
 ENTRYPOINT ["tini", "--", "/app/entrypoint.sh"]

README.md

@@ -9,11 +9,11 @@
 
 **Docs:** [Setup](https://github.com/sudolulo/winnow/wiki/Setup) · [Troubleshooting](https://github.com/sudolulo/winnow/wiki/Troubleshooting) · [FAQ](https://github.com/sudolulo/winnow/wiki/FAQ)
 
-`winnow` pulls photos from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition and object classification models.
+`winnow` pulls photos from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition.
 
-Frigate's face recognition is only as good as its training data — and the key quality metric is **diversity**, not volume. A hundred photos from the same week teach the model one lighting condition. What you need is a spread: different years, different angles, different lighting, different contexts. Your photo library already has that data. winnow finds and delivers the right subset automatically.
+The best Frigate training data is images you curate manually — photos taken specifically for recognition, in controlled conditions, uploaded directly through Frigate's UI. For people you can do that for, do it. winnow is for everyone else: people in your library you want Frigate to recognise but don't have dedicated training photos for. It mines your existing Immich library for the most diverse spread of real-world appearances and fills the gap.
 
-> **winnow only touches files it uploaded.** Faces added to Frigate manually through its UI are never deleted, replaced, or modified — not by quality replacement, not by `RESET_PERSON`, not by stale cleanup. If you have a curated training set you want to keep, it is safe.
+> **winnow only touches files it uploaded.** Faces added to Frigate manually through its UI are never deleted, replaced, or modified — not by quality replacement, not by `RESET_PERSON`, not by stale cleanup. Your manually curated images are always the primary dataset; winnow only adds to it.
 
 ---
 
@@ -39,49 +39,46 @@ Immich library
       │
       ▼
 4. Compute embeddings from the same preview thumbnails
-   • Faces   → InsightFace (ArcFace / Buffalo_L)  → 512-dim vector
-   • Objects → SigLIP (Vision Transformer)         → 768-dim vector
+   • InsightFace (ArcFace / Buffalo_L) → 512-dim vector
       │
       ▼
-5. Diversity selection
+5. Near-duplicate removal — greedy cosine-distance pass drops burst shots
+   and near-identical photos before clustering runs; the highest-quality
+   image from each near-duplicate group is kept
+      │
+      ▼
+6. Diversity selection
    • K-Medoids clustering → one representative per natural group
    • Farthest Point Sampling → fill remaining slots with maximally spread picks
-   • Hard example weighting — unusual angles and low-confidence detections
-     are biased toward selection, since those are where models tend to fail
-   • Auto mode: stops when similarity to the existing set exceeds a threshold
-     (20 % of median pairwise distance for faces, 10 % for objects)
+   • Hard example weighting — low-confidence detections get a distance boost
+     so unusual angles and harder looks are preferred over easy frontals
+   • Adaptive mode: stops when the next candidate is too similar to those already
+     selected (distance threshold = 20 % of median pairwise distance for
+     faces, 10 % for objects)
       │
       ▼
-6. Download full-resolution originals from Immich
+7. Download full-resolution originals from Immich
       │
       ▼
-7. Crop and process
-   • Face mode: EXIF-corrected, landmark-aligned 112×112 crop (ArcFace format)
-   • Object mode: YOLOv9c detection → one crop per matched instance
+8. Crop and process — EXIF-corrected, landmark-aligned 112×112 crop (ArcFace format)
       │
       ▼
-8. Deliver
-   • Face mode: upload crops to Frigate's face registration API
-     ↳ below MAX_AUTO_IMAGES — upload freely
+9. Deliver — upload crops to Frigate's face registration API
+   ↳ below MAX_AUTO_IMAGES — upload, unless the novelty gate
+     (FRIGATE_SCORE_CEILING) determines the candidate is already
+     covered by the current training set
    ↳ at cap + QUALITY_REPLACEMENT=true — with Frigate scoring active,
      swap the most redundant tracked image (highest pre-upload recognize
      score) if the candidate is more novel (lower score); falling back to
      blur-score comparison when no Frigate scores are available; manually
      added files are never touched
    ↳ at cap + QUALITY_REPLACEMENT=false — skip this person
-   • Object mode: save crops to disk → place into your Frigate data directory
 ```
 
-Uploaded and rejected asset IDs are persisted across runs. The same image is never processed twice; Frigate rejections are permanently skipped unless `RETRY_REJECTED=true`.
+Uploaded and rejected asset IDs are persisted across runs. The same image is never processed twice; rejected assets are permanently skipped unless `RETRY_REJECTED=true`.
 
 ---
 
-## Modes
-
-**Face mode** (default) — extracts face crops using Immich's bounding box metadata, applies EXIF orientation correction, and aligns them to ArcFace's standard 112×112 format using 5-point facial landmarks. Crops are uploaded directly to Frigate's face registration API.
-
-**Object mode** — runs each full-resolution image through YOLOv9c to detect instances of a target class (dog, cat, car, etc.), crops each detection, and saves it to the output directory. Frigate has no API for uploading object training data; place the crops into your Frigate data directory manually.
-
 ---
 
 ## Running in Docker
@@ -90,7 +87,7 @@ Uploaded and rejected asset IDs are persisted across runs. The same image is nev
 
 | Tag | Arch | Acceleration |
 | :-- | :-- | :-- |
-| `:latest` | amd64 + arm64 | NVIDIA CUDA 13.3 (amd64) · requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) |
+| `:latest` | amd64 + arm64 | NVIDIA CUDA 12.8 (amd64) · requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) |
 | `:rocm` | amd64 | AMD ROCm · pass `/dev/kfd` + `/dev/dri` |
 | `:intel` | amd64 | Intel Arc / iGPU via OpenVINO · pass `/dev/dri`, set `OPENVINO_DEVICE=GPU` |
 | `:cpu` | amd64 + arm64 | CPU only · ~2 GB smaller · no GPU required |
@@ -108,7 +105,7 @@ services:
       - FRIGATE_URL=http://192.168.1.10:5000
       - CRON_SCHEDULE=0 3 * * 0
     volumes:
-      - /path/to/models:/models
+      - /path/to/models:/models    # INSIGHTFACE_HOME — persists Buffalo_L model (~300 MB)
       - /path/to/cache:/app/.if_cache
       - /path/to/output:/app/frigate_train
     deploy:
@@ -154,7 +151,7 @@ See [compose.yml](compose.yml) for the full annotated example with all options.
 | *(empty string)* | Stay alive, run nothing — trigger manually with `docker exec -it winnow winnow` |
 | Cron expression | Run on startup, then repeat on schedule |
 
-In scheduled mode the process (and loaded models) stays resident between runs. The first run after a fresh install downloads the embedding models (~1–2 GB); subsequent runs use the cached models from the mounted volume.
+In scheduled mode the process (and loaded models) stays resident between runs. The first run after a fresh install downloads InsightFace Buffalo_L (~300 MB); subsequent runs use the cached model from the mounted volume.
 
 ---
 
@@ -172,11 +169,9 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `TRAINING_MODE` | `face` | `face` — upload crops to Frigate; `object` — save crops to disk |
-| `STRATEGY` | `auto` | `auto` (embedding-based adaptive), `standard` (30 images), `broad` (100 images) |
+| `STRATEGY` | `adaptive` | `adaptive` — embedding-based diversity selection, stops when candidates become redundant; `standard` — fixed 30 images; `broad` — fixed 100 images |
 | `LIMIT` | *(unset)* | Exact image count — overrides `STRATEGY` |
-| `OBJECT_CLASS` | `dog` | Target class for object mode (any YOLO class: `dog`, `cat`, `car`, etc.) |
-| `AUTO_MODE` | *(auto)* | Force non-interactive mode in a terminal; auto-detected otherwise |
+| `AUTO_MODE` | *(auto)* | Skip interactive prompts and process all people unattended — auto-detected when no TTY is present (Docker, cron); set `true` to force in a terminal |
 | `VERBOSE` | `false` | Enable DEBUG-level console output (log file is always DEBUG) |
 
 ### People Filtering
@@ -185,23 +180,33 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 | :--- | :--- | :--- |
 | `ONLY_PEOPLE` | *(unset)* | Comma-separated whitelist — process only these people |
 | `SKIP_PEOPLE` | *(unset)* | Comma-separated list — skip these people |
-| `MIN_FACE_COUNT` | `0` | Skip people with fewer than N tagged assets in Immich |
+| `MIN_FACE_COUNT` | `3` | Skip people with fewer than N tagged assets in Immich |
+| `MERGE_DUPLICATE_PEOPLE` | `false` | When Immich has duplicate entries for the same person (same face split across multiple names), merge their asset pools before processing. Without this, each duplicate group emits a warning and is skipped |
 | `YEARS_FILTER` | `10` | Ignore images older than N years |
 
+> **Duplicate people detection** — winnow warns at startup if the same name appears on multiple Immich person records (a common side-effect of Immich's face clustering creating separate pools for the same individual). By default (`false`) it logs the duplicates, keeps only the person with the most assets, and skips the rest — no data is changed. Set `MERGE_DUPLICATE_PEOPLE=true` to permanently merge each duplicate group inside Immich (the person with the most assets absorbs the others). **This modifies Immich and cannot be undone.** Only enable it once you've verified the duplicates are actually the same person.
+
 ### Image Quality
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
+| `MAX_AUTO_IMAGES` | `20` | Maximum training images per person in Frigate |
+| `QUALITY_REPLACEMENT` | `true` | When at cap, swap a weaker tracked image for a better candidate. With Frigate scoring active, targets the most redundant image (highest pre-upload recognize score); otherwise uses blur score. Never touches manually added Frigate files. Set `false` to skip people at cap |
+
+#### Advanced Tuning *(calibrated — do not adjust)*
+
+These defaults are tuned for Frigate's ArcFace requirements. winnow will warn on launch if any are set. Image quality issues caused by non-default values will not be investigated.
+
+| Variable | Default | Description |
+| :--- | :--- | :--- |
+| `ENABLE_FRIGATE_SCORES` | `true` | Call Frigate's recognize endpoint pre-upload to store diversity scores used for quality replacement. Adds ~200 ms per upload. Disabling also disables the below-cap novelty gate |
+| `FRIGATE_SCORE_CEILING` | *(unset)* | Below-cap novelty gate. Unset: dynamic — skips candidates whose Frigate score exceeds the most-redundant tracked file's score, auto-calibrates each run. `0`: disable entirely. Positive value (e.g. `0.85`): fixed hard ceiling |
 | `MIN_FACE_WIDTH` | `90` | Minimum face crop width in pixels |
 | `FACE_MARGIN` | `0.15` | Padding around bounding box crop (fraction of face size) |
 | `ENABLE_FACE_ALIGNMENT` | `true` | Align to ArcFace 112×112 format using facial landmarks |
 | `USE_FULL_RESOLUTION` | `true` | Download full-resolution originals rather than preview thumbnails |
 | `MIN_CONFIDENCE` | `0.7` | Minimum Immich face detection confidence |
 | `BLUR_THRESHOLD` | `120.0` | Laplacian variance threshold — lower accepts more blur |
-| `MAX_AUTO_IMAGES` | `80` | Maximum training images per person in Frigate |
-| `QUALITY_REPLACEMENT` | `true` | When at cap, swap a weaker tracked image for a better candidate. With Frigate scoring active, targets the most redundant image (highest pre-upload recognize score); otherwise uses blur score. Never touches manually added Frigate files. Set `false` to skip people at cap |
-| `FRIGATE_SCORE_CEILING` | `0.0` | Skip uploads whose pre-upload Frigate recognize score exceeds this value — they are already well-covered. `0` disables; requires at least one prior run to have scores |
-| `ENABLE_FRIGATE_SCORES` | `true` | Call Frigate's recognize endpoint pre-upload to store diversity scores used for quality replacement. Adds ~200 ms per upload. Disable to use blur-score replacement only |
 
 ### GPU & Models
 
@@ -211,22 +216,22 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 | `OPENVINO_DEVICE` | `CPU` | Intel variant only: set `GPU` to use Arc or iGPU; default runs on CPU |
 | `ENABLE_CACHE` | `true` | Cache computed embeddings to disk (speeds up re-runs on the same library) |
 | `CACHE_DIR` | `.if_cache` | Path for embedding cache and upload tracker files |
-| `HF_HOME` | *(system)* | HuggingFace model cache path (SigLIP) |
 | `INSIGHTFACE_HOME` | *(system)* | InsightFace model cache path (Buffalo_L) |
 
 ### Output
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `OUTPUT_DIR` | `./frigate_train` | Directory for object-mode crops and the `winnow.log` file. In Docker, set this via the volume mount instead. |
+| `OUTPUT_DIR` | `./frigate_train` | Directory where face crops are staged before upload and where `winnow.log` is written. In Docker, set this via the volume mount instead. |
 
 ### Tracker Overrides *(one-shot — remove after use)*
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
 | `DRY_RUN` | `false` | Preview selection without downloading or uploading |
-| `RETRY_REJECTED` | `false` | Re-attempt assets previously rejected by Frigate |
-| `RESET_PERSON` | *(unset)* | Clear upload history for one person and delete their winnow-managed Frigate training files so the next run starts fresh. Manually added Frigate files are never touched |
+| `RETRY_REJECTED` | `false` | Re-attempt all previously rejected assets (low-confidence skips, Frigate rejections, and other permanent exclusions) |
+| `RESET_PERSON` | *(unset)* | Set to a person's name to clear their upload history and delete their winnow-managed Frigate training files so the next run starts fresh. Set to `*` to reset all tracked people at once. Manually added Frigate files are never touched |
+| `TRACE_CROP_SIZE` | *(unset)* | Debug: print all tracked crops whose width or height matches this pixel value, then exit |
 
 ### Scheduling
 
@@ -247,14 +252,14 @@ uv run winnow
 
 Requires Python 3.13+ and [uv](https://astral.sh/uv). An NVIDIA, AMD, or Intel GPU is recommended — CPU mode works but embedding computation is slower.
 
-When run with a terminal attached, winnow starts an interactive session: select which people to process and choose a strategy (auto, standard, broad, or a custom count) per person. Without a TTY — Docker, cron, or `AUTO_MODE=true` — it processes all people automatically using the configured defaults.
+When run with a terminal attached, winnow starts an interactive session: select which people to process and choose a strategy (adaptive, standard, broad, or a custom count) per person. Without a TTY — Docker, cron, or `AUTO_MODE=true` — it processes all people unattended using the configured defaults.
 
 ---
 
 ## Requirements
 
 - **Immich** v1.106+
-- **Frigate** v0.16+ (face mode only — object mode has no Frigate dependency)
+- **Frigate** v0.16+
 - **GPU** recommended: NVIDIA (CUDA), AMD (ROCm), or Intel (Arc / iGPU via OpenVINO)
 - **Python** 3.13+
 

compose.yml

@@ -13,13 +13,9 @@ services:
       # Set AUTO_MODE=true to force auto mode in an interactive terminal.
       # To run interactively: docker exec -it winnow winnow
       # - VERBOSE=true                # Enable DEBUG-level console output
-      # TRAINING_MODE: face = upload to Frigate face recognition API
-      #                object = save crops to output dir for manual Frigate placement
-      - TRAINING_MODE=face
       # STRATEGY: auto = objective diversity (recommended), standard = 30 imgs, broad = 100 imgs
       - STRATEGY=auto
       # - LIMIT=50                    # Custom image count; overrides STRATEGY preset
-      # - OBJECT_CLASS=dog            # Object label for object mode (e.g. dog, cat, car)
 
       # ── People Filtering ──────────────────────────────────────────────────
       # - ONLY_PEOPLE=John,Jane       # Comma-separated; process only these people
@@ -35,14 +31,13 @@ services:
       # - USE_FULL_RESOLUTION=true    # Use full-res images vs thumbnails (default: true)
       # - MIN_CONFIDENCE=0.7          # Minimum face detection confidence (default: 0.7)
       # - BLUR_THRESHOLD=100.0        # Laplacian blur threshold; lower = accept more blur (default: 100.0)
-      # - MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 80)
+      # - MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 20)
 
       # ── Caching & Models ──────────────────────────────────────────────────
       # - FORCE_CPU=true              # Disable GPU, fall back to CPU
       # - OPENVINO_DEVICE=GPU         # Intel variant only: use Arc/iGPU instead of CPU (default: CPU)
       # - ENABLE_CACHE=false          # Disable embedding cache (default: true)
       - CACHE_DIR=/app/.if_cache
-      - HF_HOME=/models/huggingface
       - INSIGHTFACE_HOME=/models/.insightface
 
       # ── Tracker overrides (one-shot, remove after use) ────────────────────

pyproject-cpu.toml

@@ -1,7 +1,7 @@
 [project]
 name = "winnow"
-version = "0.2.13"
-description = "Immich to Frigate training sets"
+version = "0.4.11"
+description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
 authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }]
@@ -23,10 +23,6 @@ dependencies = [
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "rich>=14.2.0",
-    "torch>=2.12.0",
-    "torchvision>=0.27.0",
-    "transformers>=5.12.0",
-    "ultralytics>=8.4.66",
 ]
 
 [project.scripts]
@@ -34,25 +30,13 @@ winnow = "winnow.cli:main"
 
 [project.urls]
 Repository = "https://github.com/sudolulo/winnow"
+Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md"
 
 [tool.uv]
 required-environments = [
     "sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
 
-[tool.uv.sources]
-torch = [
-    { index = "pytorch-cpu", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-]
-torchvision = [
-    { index = "pytorch-cpu", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-]
-
-[[tool.uv.index]]
-name = "pytorch-cpu"
-url = "https://download.pytorch.org/whl/cpu"
-explicit = true
-
 [dependency-groups]
 dev = [
     "pytest>=8.0",
@@ -81,9 +65,6 @@ numpy = "numpy"
 onnxruntime = "onnxruntime"
 requests = "requests"
 rich = "rich"
-torch = "torch"
-transformers = "transformers"
-ultralytics = "ultralytics"
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]

pyproject-intel.toml

@@ -1,7 +1,7 @@
 [project]
 name = "winnow"
-version = "0.2.13"
-description = "Immich to Frigate training sets"
+version = "0.4.11"
+description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
 authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }]
@@ -23,10 +23,6 @@ dependencies = [
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "rich>=14.2.0",
-    "torch>=2.12.0",
-    "torchvision>=0.27.0",
-    "transformers>=5.12.0",
-    "ultralytics>=8.4.66",
 ]
 
 [project.scripts]
@@ -34,6 +30,7 @@ winnow = "winnow.cli:main"
 
 [project.urls]
 Repository = "https://github.com/sudolulo/winnow"
+Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md"
 
 [tool.uv]
 conflicts = [
@@ -47,19 +44,6 @@ required-environments = [
     "sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
 
-[tool.uv.sources]
-torch = [
-    { index = "pytorch-cpu", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-]
-torchvision = [
-    { index = "pytorch-cpu", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-]
-
-[[tool.uv.index]]
-name = "pytorch-cpu"
-url = "https://download.pytorch.org/whl/cpu"
-explicit = true
-
 [dependency-groups]
 dev = [
     "pytest>=8.0",
@@ -88,9 +72,6 @@ numpy = "numpy"
 onnxruntime-openvino = "onnxruntime"
 requests = "requests"
 rich = "rich"
-torch = "torch"
-transformers = "transformers"
-ultralytics = "ultralytics"
 
 [tool.deptry.per_rule_ignores]
 DEP002 = ["onnxruntime-openvino"]

pyproject-rocm.toml

@@ -1,7 +1,7 @@
 [project]
 name = "winnow"
-version = "0.2.13"
-description = "Immich to Frigate training sets"
+version = "0.4.11"
+description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
 authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }]
@@ -23,10 +23,6 @@ dependencies = [
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "rich>=14.2.0",
-    "torch>=2.5.0",
-    "torchvision>=0.20.0",
-    "transformers>=5.12.0",
-    "ultralytics>=8.4.66",
 ]
 
 [project.scripts]
@@ -34,6 +30,7 @@ winnow = "winnow.cli:main"
 
 [project.urls]
 Repository = "https://github.com/sudolulo/winnow"
+Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md"
 
 [tool.uv]
 index-strategy = "unsafe-best-match"
@@ -48,18 +45,6 @@ required-environments = [
     "sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
 
-[tool.uv.sources]
-torch = [
-    { index = "pytorch-rocm63", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-]
-torchvision = [
-    { index = "pytorch-rocm63", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-]
-
-[[tool.uv.index]]
-name = "pytorch-rocm63"
-url = "https://download.pytorch.org/whl/rocm6.3"
-
 [dependency-groups]
 dev = [
     "pytest>=8.0",
@@ -88,9 +73,6 @@ numpy = "numpy"
 onnxruntime-rocm = "onnxruntime"
 requests = "requests"
 rich = "rich"
-torch = "torch"
-transformers = "transformers"
-ultralytics = "ultralytics"
 
 [tool.deptry.per_rule_ignores]
 DEP002 = ["onnxruntime-rocm"]

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "winnow"
-version = "0.4.7"
-description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition and object classification."
+version = "0.4.11"
+description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
 authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }]
@@ -27,10 +27,6 @@ dependencies = [
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "rich>=14.2.0",
-    "torch>=2.12.0",
-    "torchvision>=0.27.0",
-    "transformers>=5.12.0",
-    "ultralytics>=8.4.66",
 ]
 
 [project.scripts]
@@ -53,27 +49,6 @@ required-environments = [
     "sys_platform == 'linux' and platform_machine == 'aarch64'",
 ]
 
-[tool.uv.sources]
-torch = [
-    { index = "pytorch-cu126", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-    { index = "pytorch-cpu", marker = "sys_platform == 'linux' and platform_machine == 'aarch64'" },
-    { index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
-]
-torchvision = [
-    { index = "pytorch-cu126", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
-    { index = "pytorch-cpu", marker = "sys_platform == 'linux' and platform_machine == 'aarch64'" },
-    { index = "pytorch-cpu", marker = "sys_platform != 'linux'" },
-]
-
-[[tool.uv.index]]
-name = "pytorch-cu126"
-url = "https://download.pytorch.org/whl/cu126"
-explicit = true
-
-[[tool.uv.index]]
-name = "pytorch-cpu"
-url = "https://download.pytorch.org/whl/cpu"
-explicit = true
 
 [dependency-groups]
 dev = [
@@ -104,9 +79,6 @@ nvidia-cudnn-cu12 = "nvidia.cudnn"
 onnxruntime-gpu = "onnxruntime"
 requests = "requests"
 rich = "rich"
-torch = "torch"
-transformers = "transformers"
-ultralytics = "ultralytics"
 
 [tool.deptry.per_rule_ignores]
 DEP002 = ["onnxruntime-gpu", "nvidia-cudnn-cu12"]

scheduler.py

@@ -16,7 +16,6 @@ except ImportError:
 from winnow.cli import main
 
 SCHEDULE = os.environ["CRON_SCHEDULE"]
-MODELS_DIR = os.environ.get("HF_HOME", "/models/huggingface")
 INSIGHTFACE_HOME = os.environ.get("INSIGHTFACE_HOME", "/models/.insightface")
 
 logger = logging.getLogger(__name__)
@@ -24,11 +23,8 @@ logger = logging.getLogger(__name__)
 
 def check_models() -> None:
     buffalo = Path(INSIGHTFACE_HOME) / "models" / "buffalo_l"
-    hf_hub = Path(MODELS_DIR) / "hub"
     if not buffalo.exists():
         print("  InsightFace Buffalo_L not found — will download on first run", flush=True)
-    if not (hf_hub.exists() and any(hf_hub.iterdir())):
-        print("  HuggingFace models not found — will download on first run", flush=True)
 
 
 NOW = time.time()

scripts/benchmark.py

@@ -2,8 +2,8 @@
 """
 winnow inference benchmark: GPU vs CPU throughput.
 
-Measures InsightFace (face mode) and SigLIP (object mode) latency and
-throughput. Run with FORCE_CPU=true for CPU-only baseline.
+Measures InsightFace (ArcFace) latency and throughput.
+Run with FORCE_CPU=true for CPU-only baseline.
 
 Usage inside container:
     # GPU mode:
@@ -47,11 +47,6 @@ def make_face_image(size: int = 640) -> Image.Image:
     return img
 
 
-def make_random_image(width: int = 224, height: int = 224) -> Image.Image:
-    rng = np.random.default_rng(42)
-    return Image.fromarray(rng.integers(0, 256, (height, width, 3), dtype=np.uint8), "RGB")
-
-
 def _stats(times_s: list[float]) -> dict:
     arr = np.array(times_s) * 1000  # ms
     return {
@@ -119,61 +114,6 @@ def bench_insightface(n_warmup: int = 5, n_runs: int = 30) -> None:
     print(f"  320×320 median  : {s2['median_ms']:.1f} ms  ({s2['ips']:.1f} img/s)")
 
 
-def bench_siglip(
-    n_warmup: int = 3,
-    n_runs: int = 20,
-    batch_sizes: tuple = (1, 4, 8, 16, 32),
-) -> None:
-    import torch
-
-    import winnow.embeddings as emb_mod
-    emb_mod._siglip_model = None
-    emb_mod._siglip_processor = None
-    emb_mod._siglip_loaded = False
-
-    print("  Loading model...")
-    t_load = time.perf_counter()
-    model, processor = emb_mod.get_siglip_model()
-    load_s = time.perf_counter() - t_load
-
-    if model is None:
-        print("  SKIP: SigLIP failed to load")
-        return
-
-    device = next(model.parameters()).device
-    print(f"  Model load time : {load_s:.2f} s  (device: {device})")
-
-    print(f"  {'Batch':>5}  {'ms/batch':>10}  {'ms/img':>8}  {'img/s':>8}  {'p95/img':>9}")
-    for bs in batch_sizes:
-        imgs = [make_random_image(224, 224) for _ in range(bs)]
-        inputs = processor(images=imgs, return_tensors="pt")
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        # Warmup
-        for _ in range(n_warmup):
-            with torch.no_grad():
-                model(**inputs)
-        if str(device) != "cpu":
-            torch.cuda.synchronize()
-
-        times: list[float] = []
-        for _ in range(n_runs):
-            if str(device) != "cpu":
-                torch.cuda.synchronize()
-            t0 = time.perf_counter()
-            with torch.no_grad():
-                model(**inputs)
-            if str(device) != "cpu":
-                torch.cuda.synchronize()
-            times.append(time.perf_counter() - t0)
-
-        s = _stats(times)
-        print(
-            f"  {bs:>5}  {s['median_ms']:>10.1f}  {s['median_ms']/bs:>8.2f}"
-            f"  {bs * 1000 / s['median_ms']:>8.1f}  {s['p95_ms']/bs:>9.2f}"
-        )
-
-
 def main() -> None:
     print("=" * 56)
     print("  winnow inference benchmark")
@@ -185,10 +125,6 @@ def main() -> None:
     bench_insightface()
     print()
 
-    print("── SigLIP google/siglip-base-patch16-224  (objects) ───")
-    bench_siglip()
-    print()
-
 
 if __name__ == "__main__":
     # Add winnow to path when run directly inside container

tests/test_config.py

@@ -18,10 +18,10 @@ def test_config_loads_defaults(monkeypatch):
     assert cfg.OUTPUT_DIR == "./frigate_train"
     assert cfg.YEARS_FILTER == 10
     assert cfg.MIN_FACE_WIDTH == 90
-    assert cfg.MIN_FACE_COUNT == 0
+    assert cfg.MIN_FACE_COUNT == 3
     assert cfg.BLUR_THRESHOLD == 120.0
     assert cfg.MIN_CONFIDENCE == 0.7
-    assert cfg.MAX_AUTO_IMAGES == 80
+    assert cfg.MAX_AUTO_IMAGES == 20
     assert cfg.QUALITY_REPLACEMENT is True
     assert cfg.FACE_MARGIN == 0.15
     assert cfg.USE_FULL_RESOLUTION is True

winnow/__init__.py

@@ -1,8 +1,7 @@
 """Immich to Frigate training set curator.
 
 AI-powered tool to extract high-quality, diverse training images from your
-Immich library for Frigate's Face Recognition (ArcFace) and Object/State
-Classification models.
+Immich library for Frigate's face recognition (ArcFace/Buffalo_L).
 """
 
 from importlib.metadata import PackageNotFoundError, version

winnow/cache.py

@@ -15,7 +15,6 @@ logger = logging.getLogger(__name__)
 # Model versions — bump these when the upstream model changes
 MODEL_VERSIONS = {
     "insightface": "buffalo_l_v1",
-    "siglip": "siglip-base-patch16-224_v1",
     "immich": "immich_buffalo_l_v1",
 }
 

winnow/cli.py

@@ -130,6 +130,18 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
     return people
 
 
+_UNSUPPORTED_VARS = [
+    "ENABLE_FRIGATE_SCORES",
+    "FRIGATE_SCORE_CEILING",
+    "MIN_FACE_WIDTH",
+    "FACE_MARGIN",
+    "ENABLE_FACE_ALIGNMENT",
+    "USE_FULL_RESOLUTION",
+    "MIN_CONFIDENCE",
+    "BLUR_THRESHOLD",
+]
+
+
 def main() -> None:
     """Entry point for winnow CLI."""
     try:
@@ -145,6 +157,17 @@ def main() -> None:
     [dim]Immich -> Frigate Training Data Curator[/dim]
         """)
 
+        set_unsupported = [v for v in _UNSUPPORTED_VARS if os.environ.get(v)]
+        if set_unsupported:
+            console.print(
+                f"[bold yellow]⚠  Advanced tuning vars set: "
+                f"{', '.join(set_unsupported)}[/bold yellow]"
+            )
+            console.print(
+                "[dim]  These defaults are calibrated for Frigate's ArcFace requirements. "
+                "Image quality issues caused by non-default values will not be investigated.[/dim]\n"
+            )
+
         ConfigManager.get().interactive_setup()
 
         try:

winnow/config.py

@@ -29,13 +29,13 @@ class _Config:
     MIN_FACE_WIDTH: int = 90
     BLUR_THRESHOLD: float = 120.0
     MIN_CONFIDENCE: float = 0.7
-    MAX_AUTO_IMAGES: int = 80
+    MAX_AUTO_IMAGES: int = 20
     QUALITY_REPLACEMENT: bool = True
-    FRIGATE_SCORE_CEILING: float = 0.0
+    FRIGATE_SCORE_CEILING: float | None = None
     ENABLE_FRIGATE_SCORES: bool = True
 
     # People filtering
-    MIN_FACE_COUNT: int = 0
+    MIN_FACE_COUNT: int = 3
     MERGE_DUPLICATE_PEOPLE: bool = False
 
     # Output quality
@@ -60,13 +60,14 @@ class _Config:
         self.OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./frigate_train")
         self.YEARS_FILTER = int(os.getenv("YEARS_FILTER", "10"))
         self.MIN_FACE_WIDTH = int(os.getenv("MIN_FACE_WIDTH", "90"))
-        self.MIN_FACE_COUNT = int(os.getenv("MIN_FACE_COUNT", "0"))
+        self.MIN_FACE_COUNT = int(os.getenv("MIN_FACE_COUNT", "3"))
         self.MERGE_DUPLICATE_PEOPLE = os.getenv("MERGE_DUPLICATE_PEOPLE", "false").lower() in ("true", "1", "yes")
         self.BLUR_THRESHOLD = float(os.getenv("BLUR_THRESHOLD", "120.0"))
         self.MIN_CONFIDENCE = float(os.getenv("MIN_CONFIDENCE", "0.7"))
-        self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "80"))
+        self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "20"))
         self.QUALITY_REPLACEMENT = os.getenv("QUALITY_REPLACEMENT", "true").lower() in ("true", "1", "yes")
-        self.FRIGATE_SCORE_CEILING = float(os.getenv("FRIGATE_SCORE_CEILING", "0.0"))
+        _ceiling_env = os.getenv("FRIGATE_SCORE_CEILING", "").strip()
+        self.FRIGATE_SCORE_CEILING = float(_ceiling_env) if _ceiling_env else None
         self.ENABLE_FRIGATE_SCORES = os.getenv("ENABLE_FRIGATE_SCORES", "true").lower() in ("true", "1", "yes")
         self.FACE_MARGIN = float(os.getenv("FACE_MARGIN", "0.15"))
         self.USE_FULL_RESOLUTION = os.getenv("USE_FULL_RESOLUTION", "true").lower() in ("true", "1", "yes")

winnow/diversity.py

@@ -5,7 +5,7 @@ Selection pipeline:
 1. Concurrent thumbnail download
 2. Quality filtering (blur, IR, exposure, confidence, face size)
 3. Face crop extraction (embed person's face, not full image)
-4. Embedding computation (InsightFace or SigLIP)
+4. Embedding computation (InsightFace)
 5. Cluster-aware selection (K-Medoids + FPS with hard example weighting)
 """
 
@@ -28,7 +28,6 @@ def select_diverse_assets(
     limit: int | str,
     entity_name: str,
     selection_mode: str = "smart",
-    entity_type: str = "face",
     person_id: str | None = None,
     progress_callback=None,
 ) -> list:
@@ -38,9 +37,8 @@ def select_diverse_assets(
     Args:
         assets: List of asset dicts from Immich API
         limit: Number to select, or "auto" for dynamic selection
-        entity_name: Name of the person/object for logging
+        entity_name: Name of the person for logging
         selection_mode: 'smart' (embedding-based) or 'time' (time spread)
-        entity_type: 'face' or 'object' - determines embedding model
         progress_callback: Optional callback(current, total) for progress
 
     Returns:
@@ -53,14 +51,13 @@ def select_diverse_assets(
     # Sort by creation time
     assets = sorted(assets, key=lambda x: x.get("fileCreatedAt", ""))
 
-    if selection_mode != "smart" or not is_embedding_available(entity_type):
+    if selection_mode != "smart" or not is_embedding_available():
         if selection_mode == "smart":
-            model_name = "InsightFace" if entity_type == "face" else "SigLIP"
-            logger.warning(f"{model_name} unavailable. Falling back to time spread.")
+            logger.warning("InsightFace unavailable. Falling back to time spread.")
         return _select_time_spread(assets, limit)
 
     try:
-        return _select_by_embedding(assets, limit, entity_type, person_id, progress_callback)
+        return _select_by_embedding(assets, limit, person_id, progress_callback)
     except Exception as e:
         logger.error(f"Smart Diversity failed: {e}. Falling back to time spread.")
         return _select_time_spread(assets, limit)
@@ -179,7 +176,6 @@ def _crop_face_from_thumbnail(
 def _select_by_embedding(
     assets: list,
     limit: int | str,
-    entity_type: str,
     person_id: str | None = None,
     progress_callback=None,
 ) -> list:
@@ -188,7 +184,7 @@ def _select_by_embedding(
     Pipeline:
     1. Concurrent thumbnail download
     2. Quality filtering
-    3. Face crop extraction (face mode only)
+    3. Face crop extraction
     4. Embedding computation
     5. Cluster-aware selection with hard example weighting
     """
@@ -243,7 +239,6 @@ def _select_by_embedding(
 
             confidence = _get_face_confidence(asset, person_id=person_id)
 
-            if entity_type == "face":
             face_bbox = _get_face_bbox(asset, person_id=person_id)
             quality = assess_quality(
                 img,
@@ -261,10 +256,8 @@ def _select_by_embedding(
             asset["quality_score"] = quality.blur_score
             face_crop = _crop_face_from_thumbnail(img, asset, person_id=person_id)
             embed_img = face_crop if face_crop is not None else img
-            else:
-                embed_img = img
 
-            emb = get_embedding(embed_img, entity_type, asset_id=asset["id"])
+            emb = get_embedding(embed_img, asset_id=asset["id"])
             if emb is not None:
                 embeddings.append(emb)
                 valid_candidates.append(asset)
@@ -300,7 +293,6 @@ def _select_by_embedding(
         embeddings,
         valid_candidates,
         limit,
-        entity_type=entity_type,
         confidence_scores=confidence_scores,
     )
 
@@ -429,11 +421,11 @@ def _kmedoids(dist_matrix: np.ndarray, k: int, max_iter: int = 50) -> tuple[list
 # =============================================================================
 
 
-def _compute_adaptive_threshold(emb_normed: np.ndarray, entity_type: str) -> float:
+def _compute_adaptive_threshold(emb_normed: np.ndarray) -> float:
     """Compute adaptive FPS stop threshold based on actual embedding distribution.
 
     Instead of a hardcoded threshold, samples pairwise distances and sets
-    the threshold as a fraction of the median pairwise distance.
+    the threshold as 20% of the median pairwise distance.
     """
     n = len(emb_normed)
     sample_size = min(200, n)
@@ -441,22 +433,14 @@ def _compute_adaptive_threshold(emb_normed: np.ndarray, entity_type: str) -> flo
     indices = rng.choice(n, sample_size, replace=False) if n > sample_size else np.arange(n)
     sample = emb_normed[indices]
 
-    # Compute pairwise cosine distances for the sample
     pairwise = 1 - sample @ sample.T
     upper_tri = pairwise[np.triu_indices(len(sample), k=1)]
     if len(upper_tri) == 0:
         return 0.05
     median_dist = float(np.median(upper_tri))
+    threshold = max(0.05, median_dist * 0.20)
 
-    # Faces: 20% of median (tighter — want fewer, more distinct images)
-    # Objects: 10% of median (wider — want more diversity)
-    fraction = 0.20 if entity_type == "face" else 0.10
-    threshold = max(0.05, median_dist * fraction)
-
-    logger.debug(
-        f"Adaptive threshold: {threshold:.4f} "
-        f"(median_dist={median_dist:.4f}, fraction={fraction}, type={entity_type})"
-    )
+    logger.debug(f"Adaptive threshold: {threshold:.4f} (median_dist={median_dist:.4f})")
     return threshold
 
 
@@ -464,7 +448,6 @@ def _cluster_aware_selection(
     embeddings: list,
     candidates: list,
     limit: int | str,
-    entity_type: str = "face",
     confidence_scores: list | None = None,
 ) -> list:
     """Two-stage selection: K-Medoids clustering → FPS with hard example weighting.
@@ -484,13 +467,13 @@ def _cluster_aware_selection(
 
     # Build confidence weight array for hard example boosting
     conf_array = np.ones(n)
-    if confidence_scores and entity_type == "face":
+    if confidence_scores:
         for i, c in enumerate(confidence_scores):
             if c is not None:
                 conf_array[i] = c
 
     # Compute adaptive threshold for auto mode
-    auto_threshold = _compute_adaptive_threshold(emb_normed, entity_type) if limit == "auto" else 0.0
+    auto_threshold = _compute_adaptive_threshold(emb_normed) if limit == "auto" else 0.0
     target = Config.MAX_AUTO_IMAGES if limit == "auto" else limit
 
     # --- Stage 1: K-Medoids clustering ---
@@ -542,15 +525,9 @@ def _cluster_aware_selection(
         min_dists = np.minimum(min_dists, dists_to_new)
         min_dists[best_idx] = -np.inf
 
-    # Log hard example stats
-    if entity_type == "face":
     selected_conf = [conf_array[i] for i in selected if conf_array[i] < 1.0]
     hard_count = sum(1 for c in selected_conf if c < 0.85)
-        logger.info(
-            f"Selection complete: {len(selected)} images " f"({hard_count} hard examples with confidence < 0.85)."
-        )
-    else:
-        logger.info(f"Selection complete: {len(selected)} diverse images.")
+    logger.info(f"Selection complete: {len(selected)} images ({hard_count} hard examples with confidence < 0.85).")
 
     return [candidates[i] for i in selected]
 

winnow/embeddings.py

@@ -1,8 +1,7 @@
 """
-Unified embedding interface for faces and objects.
+Embedding interface for face diversity selection.
 
 - Faces: InsightFace (ArcFace/Buffalo_L) — or reuse from Immich
-- Objects: SigLIP (Vision Transformer via transformers)
 - Caching: Disk-based cache avoids recomputation on reruns
 """
 
@@ -42,12 +41,9 @@ def _suppress_output():
         os.close(saved_err)
 
 
-# Lazy-loaded singletons
+# Lazy-loaded singleton
 _insightface_app = None
 _insightface_loaded = False
-_siglip_model = None
-_siglip_processor = None
-_siglip_loaded = False
 
 
 def _is_force_cpu() -> bool:
@@ -202,169 +198,33 @@ def get_face_embedding(img_pil: Image.Image) -> np.ndarray | None:
 
 
 # =============================================================================
-# SigLIP (Objects)
-# =============================================================================
-
-
-def get_siglip_model():
-    """Singleton for SigLIP model and processor with GPU auto-detection."""
-    global _siglip_model, _siglip_processor, _siglip_loaded
-    if _siglip_loaded:
-        return _siglip_model, _siglip_processor
-    _siglip_loaded = True
-
-    try:
-        import warnings
-
-        import torch
-        from transformers import AutoImageProcessor, SiglipVisionModel
-
-        model_name = "google/siglip-base-patch16-224"
-
-        # Disk cache check — path derived from model_name using HuggingFace's slug convention
-        hf_home = os.environ.get("HF_HOME", os.path.join(os.path.expanduser("~"), ".cache", "huggingface"))
-        cache_slug = "models--" + model_name.replace("/", "--")
-        model_cache = Path(hf_home) / "hub" / cache_slug
-        if model_cache.exists() and any(model_cache.iterdir()):
-            logger.info(f"SigLIP {model_name}: found in model cache")
-        else:
-            logger.info(f"SigLIP {model_name}: not cached — downloading now (~380 MB)")
-
-        logger.info(f"SigLIP {model_name}: loading into memory...")
-        t0 = time.time()
-
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=FutureWarning)
-            warnings.filterwarnings("ignore", message=".*use_fast.*")
-            _siglip_processor = AutoImageProcessor.from_pretrained(model_name, use_fast=True)
-            _siglip_model = SiglipVisionModel.from_pretrained(model_name)
-
-        _siglip_model.eval()
-
-        # Move to GPU if available (ROCm builds expose torch.cuda.is_available() == True)
-        if not _is_force_cpu():
-            if torch.cuda.is_available():
-                _siglip_model = _siglip_model.cuda()
-                device_name = "CUDA GPU"
-            elif hasattr(torch, "xpu") and torch.xpu.is_available():
-                _siglip_model = _siglip_model.to("xpu")
-                device_name = "Intel XPU"
-            elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-                _siglip_model = _siglip_model.to("mps")
-                device_name = "Apple MPS"
-            else:
-                device_name = "CPU"
-        else:
-            device_name = "CPU (FORCE_CPU)"
-
-        logger.info(f"SigLIP {model_name}: ready on {device_name} ({time.time() - t0:.1f}s)")
-        return _siglip_model, _siglip_processor
-
-    except ImportError as e:
-        logger.error(f"transformers/torch not installed: {e}")
-        return None, None
-    except Exception as e:
-        logger.error(f"Failed to load SigLIP: {e}")
-        return None, None
-
-
-def get_object_embedding(img_pil: Image.Image) -> np.ndarray | None:
-    """Get 768-dim SigLIP embedding for an image."""
-    model, processor = get_siglip_model()
-    if model is None:
-        return None
-
-    try:
-        import torch
-
-        inputs = processor(images=img_pil, return_tensors="pt")
-        device = next(model.parameters()).device
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        with torch.no_grad():
-            outputs = model(**inputs)
-            return outputs.pooler_output.squeeze().cpu().numpy()
-    except Exception as e:
-        logger.error(f"Error getting object embedding: {e}")
-        return None
-
-
-def get_object_embeddings_batch(images: list[Image.Image]) -> list[np.ndarray | None]:
-    """Get SigLIP embeddings for a batch of images (GPU-efficient)."""
-    model, processor = get_siglip_model()
-    if model is None:
-        return [None] * len(images)
-
-    try:
-        import torch
-
-        inputs = processor(images=images, return_tensors="pt", padding=True)
-        device = next(model.parameters()).device
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        with torch.no_grad():
-            outputs = model(**inputs)
-            embeddings = outputs.pooler_output.cpu().numpy()
-            return [embeddings[i] for i in range(len(embeddings))]
-    except Exception as e:
-        logger.error(f"Error in batch embedding: {e}")
-        # Fall back to individual computation
-        return [get_object_embedding(img) for img in images]
-
-
-# =============================================================================
-# Unified Interface with Caching
+# Embedding Interface with Caching
 # =============================================================================
 
 
 def get_embedding(
     img_pil: Image.Image,
-    entity_type: str = "face",
     asset_id: str | None = None,
-    immich_embedding: np.ndarray | None = None,
 ) -> np.ndarray | None:
-    """Get embedding for an image based on entity type.
+    """Get embedding for a face image.
 
-    Priority:
-    1. Pre-fetched Immich embedding (if provided)
-    2. Disk cache (if enabled and asset_id provided)
-    3. Local model computation (InsightFace or SigLIP)
-
-    Args:
-        img_pil: The image to embed
-        entity_type: 'face' or 'object'
-        asset_id: Optional asset ID for cache lookup
-        immich_embedding: Optional pre-fetched embedding from Immich API
+    Checks disk cache first (if enabled and asset_id provided),
+    then falls back to local InsightFace computation.
     """
     from .config import Config
 
     use_cache = Config.ENABLE_CACHE and asset_id is not None
     cache = get_cache(Config.CACHE_DIR) if use_cache else None
-    # Use a single consistent cache key per model so lookups and stores always match.
-    # "immich" was previously used as the face key on the lookup path but "insightface"
-    # on the store path — meaning the cache was never hit for locally-computed embeddings.
-    cache_key = "insightface" if entity_type == "face" else "siglip"
 
-    # 1. Use Immich embedding if provided
-    if immich_embedding is not None:
     if cache:
-            cache.put(asset_id, immich_embedding, cache_key)
-        return immich_embedding
-
-    # 2. Check disk cache
-    if cache:
-        cached = cache.get(asset_id, cache_key)
+        cached = cache.get(asset_id, "insightface")
         if cached is not None:
             return cached
 
-    # 3. Compute locally
-    if entity_type == "face":
     emb = get_face_embedding(img_pil)
-    else:
-        emb = get_object_embedding(img_pil)
 
     if emb is not None and cache:
-        cache.put(asset_id, emb, cache_key)
+        cache.put(asset_id, emb, "insightface")
 
     return emb
 
@@ -378,36 +238,18 @@ def _is_module_available(module_name: str) -> bool:
         return False
 
 
-def is_embedding_available(entity_type: str = "face", *, load: bool = False) -> bool:
-    """Check if embedding model is available for the given entity type.
+def is_embedding_available(*, load: bool = False) -> bool:
+    """Check if InsightFace is available.
 
     By default this performs a lightweight import-check only (no model loading).
-    Pass ``load=True`` to actually load the model (expensive, hundreds of MB).
-
-    Args:
-        entity_type: 'face' or 'object'
-        load: If True, fully load the model to verify. If False (default),
-              only check that the required packages are importable.
+    Pass ``load=True`` to actually load the model (expensive, ~300 MB).
     """
     if load:
-        if entity_type == "face":
         return get_insightface_app() is not None
-        model, _ = get_siglip_model()
-        return model is not None
-
-    # Lightweight check: just verify the packages are importable
-    if entity_type == "face":
     return _is_module_available("insightface") and _is_module_available("onnxruntime")
-    return _is_module_available("transformers") and _is_module_available("torch")
 
 
-def load_embedding_model(entity_type: str = "face") -> bool:
-    """Explicitly load the embedding model for the given entity type.
-
-    Returns True if the model loaded successfully.
-    """
-    if entity_type == "face":
+def load_embedding_model() -> bool:
+    """Explicitly load InsightFace. Returns True if the model loaded successfully."""
     return get_insightface_app() is not None
-    model, _ = get_siglip_model()
-    return model is not None
 

winnow/executor.py

@@ -19,7 +19,7 @@ from .frigate_api import (
     get_frigate_person_files,
     recognize_face,
 )
-from .image_processing import process_face_mode, process_full_mode, process_object_mode
+from .image_processing import process_face_mode
 from .immich_api import fetch_face_data, fetch_full_image
 from .log_config import console
 from .quality import assess_quality
@@ -31,7 +31,6 @@ from .upload_tracker import (
     has_frigate_scores,
     mark_rejected,
     mark_uploaded,
-    record_frigate_file,
     record_frigate_files_batch,
     remove_frigate_file,
 )
@@ -174,11 +173,11 @@ def execute_jobs(jobs: list[dict]) -> None:
 
     use_full_res = Config.USE_FULL_RESOLUTION
 
-    # Load InsightFace app for landmark-based crop alignment (face mode only).
+    # Load InsightFace app for landmark-based crop alignment.
     # The model is already resident from the diversity/embedding phase, so this
     # is just a singleton lookup — no load cost.
     insightface_app = None
-    if any(j["config"].get("mode", "face") == "face" for j in jobs) and Config.ENABLE_FACE_ALIGNMENT:
+    if Config.ENABLE_FACE_ALIGNMENT:
         try:
             from .embeddings import get_insightface_app
 
@@ -197,8 +196,8 @@ def execute_jobs(jobs: list[dict]) -> None:
         overall_task = progress.add_task("[green]Overall Progress", total=grand_total)
 
         for job in jobs:
-            person, assets, config = job["person"], job["assets"], job["config"]
-            name, mode = person["name"], config.get("mode", "face")
+            person, assets = job["person"], job["assets"]
+            name = person["name"]
 
             job_task = progress.add_task(f"Processing {name}...", total=len(assets))
             try:
@@ -207,8 +206,7 @@ def execute_jobs(jobs: list[dict]) -> None:
                 logger.error(str(e))
                 continue
             # Face crops are transient (uploaded then discarded); wipe before each run.
-            # Object crops are the deliverable; preserve them across runs.
-            if mode == "face" and os.path.isdir(person_dir):
+            if os.path.isdir(person_dir):
                 shutil.rmtree(person_dir)
             os.makedirs(person_dir, exist_ok=True)
 
@@ -220,9 +218,8 @@ def execute_jobs(jobs: list[dict]) -> None:
             count = 0
             for asset in assets:
                 try:
-                    # For face mode, enrich the asset with face bounding box data
-                    # from the Immich faces API (not included in search/metadata results)
-                    if mode == "face":
+                    # Enrich the asset with face bounding box data from the Immich
+                    # faces API (not included in search/metadata results).
                     asset = _enrich_asset_with_face_data(asset, person)
                     # Skip download if detection confidence already disqualifies
                     # the asset — avoids fetching a large image we'll discard.
@@ -232,6 +229,7 @@ def execute_jobs(jobs: list[dict]) -> None:
                             f"[yellow]Skipped {asset['id']}"
                             f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
                         )
+                        mark_rejected(asset["id"], person_name=name)
                         progress.advance(job_task)
                         progress.advance(overall_task)
                         continue
@@ -250,26 +248,21 @@ def execute_jobs(jobs: list[dict]) -> None:
                     if img is None:
                         progress.console.print(f"[red]Failed download {asset['id']}[/red]")
                     else:
-                        saved = (
-                            process_face_mode(img, asset, person, person_dir, count, insightface_app=insightface_app)
-                            if mode == "face"
-                            else process_object_mode(img, config, person_dir, count)
-                            if mode == "object"
-                            else process_full_mode(img, person_dir, count)
+                        saved = process_face_mode(
+                            img, asset, person, person_dir, count, insightface_app=insightface_app
                         )
                         if saved:
-                            # Record which asset produced which output file
                             filename = f"{count}.jpg"
                             asset_map[filename] = asset["id"]
                             score_map[filename] = asset.get("quality_score")
-                            if mode == "face" and isinstance(saved, tuple):
+                            if isinstance(saved, tuple):
                                 dims_map[filename] = saved
                             # Time-spread path: compute blur score from the downloaded
                             # image. Cap at 1440px so the scale matches the preview
                             # thumbnails the embedding path uses for scoring — Laplacian
                             # variance grows with resolution, making full-res and
                             # thumbnail scores incomparable if left uncapped.
-                            if mode == "face" and score_map[filename] is None:
+                            if score_map[filename] is None:
                                 try:
                                     score_img = img.convert("RGB") if img.mode != "RGB" else img
                                     if score_img.width > 1440 or score_img.height > 1440:
@@ -279,12 +272,6 @@ def execute_jobs(jobs: list[dict]) -> None:
                                 except Exception as exc:
                                     logger.debug(f"Quality score fallback for {asset['id']}: {exc}")
                                     score_map[filename] = 0.0  # unknown quality — treat as lowest
-                            # Also record object-mode variant filenames
-                            if mode == "object":
-                                for f in sorted(os.listdir(person_dir)):
-                                    if f.startswith(f"{count}_") and f not in asset_map:
-                                        asset_map[f] = asset["id"]
-                                        score_map[f] = asset.get("face_confidence")
 
                             count += 1
                         else:
@@ -312,29 +299,13 @@ def execute_jobs(jobs: list[dict]) -> None:
 def upload_to_frigate(jobs: list[dict]) -> None:
     """Upload processed face crops to Frigate via API with detailed logging.
 
-    Only runs for face-mode jobs. Object-mode crops are saved to the output
-    directory as the deliverable and must be copied to Frigate manually.
-
     After each successful upload, records the Immich asset ID in the
     upload tracker so it is skipped on future runs.
     """
-    face_jobs = [j for j in jobs if j["config"].get("mode", "face") == "face"]
-
-    if not face_jobs:
-        rprint("[dim]No face-mode jobs to upload.[/dim]")
+    if not jobs:
+        rprint("[dim]No jobs to upload.[/dim]")
         return
 
-    # Notify user about object-mode jobs that were skipped
-    object_jobs = [j for j in jobs if j["config"].get("mode") == "object"]
-    for job in object_jobs:
-        name = job["person"]["name"]
-        try:
-            person_dir = _safe_person_dir(Config.OUTPUT_DIR, name)
-        except ValueError as e:
-            logger.error(str(e))
-            continue
-        rprint(f"  [dim]📁 {name} (object): crops saved to {person_dir} — copy to Frigate manually[/dim]")
-
     frigate_url = os.environ.get("FRIGATE_URL", "")
     if not frigate_url:
         rprint("[yellow]⚠️  FRIGATE_URL not set, skipping upload.[/yellow]")
@@ -347,7 +318,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
     # from the asset_map stored on each job during execute_jobs()
     filename_to_asset_id: dict[str, dict[str, str]] = {}
     total_files = 0
-    for job in face_jobs:
+    for job in jobs:
         name = job["person"]["name"]
         asset_map = job.get("asset_map", {})
         filename_to_asset_id[name] = asset_map
@@ -357,7 +328,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
         rprint("  [yellow]No images found to upload.[/yellow]")
         return
 
-    rprint(f"  People: [bold]{len(face_jobs)}[/bold], Total images: [bold]{total_files}[/bold]")
+    rprint(f"  People: [bold]{len(jobs)}[/bold], Total images: [bold]{total_files}[/bold]")
 
     uploaded, failed = 0, 0
     max_retries = 2
@@ -375,7 +346,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
     ) as progress:
         upload_task = progress.add_task("[green]Uploading to Frigate", total=total_files)
 
-        for job in face_jobs:
+        for job in jobs:
             name = job["person"]["name"]
             # URL-encode the name for the API (handles spaces, special chars)
             encoded_name = quote(name, safe="")
@@ -483,14 +454,28 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         if _result is not None and (_result[0] or "").casefold() == name.casefold():
                             pre_fscore = _result[1]
 
-                # Ceiling check: skip if the existing training set already covers this
-                # face condition well. Applies below cap only — at cap, replacement logic
-                # drives the decision.
-                if not at_cap and Config.FRIGATE_SCORE_CEILING > 0 and pre_run_count > 0:
-                    if pre_fscore is not None and pre_fscore > Config.FRIGATE_SCORE_CEILING:
+                # Below-cap novelty gate: skip candidates already covered by the Frigate model,
+                # including conditions learned from manually-added images winnow can't track.
+                # pre_fscore is None on the first run (pre_run_count == 0 skips recognize_face
+                # above), so this block never fires on the first run without an extra guard.
+                if not at_cap and pre_fscore is not None:
+                    _ceiling = Config.FRIGATE_SCORE_CEILING
+                    if _ceiling is None:
+                        # Dynamic default: bar = most-redundant tracked file's Frigate score.
+                        # Falls back to uploading freely when no tracked scores exist yet.
+                        _bar = get_most_redundant_mapped_file(name)
+                        _skip = _bar is not None and pre_fscore > _bar[2]
+                        _bar_str = f"most redundant tracked {_bar[2]:.2f}" if _bar else ""
+                    elif _ceiling == 0.0:
+                        _skip = False  # explicitly disabled
+                        _bar_str = ""
+                    else:
+                        _skip = pre_fscore > _ceiling
+                        _bar_str = f"ceiling {_ceiling:.2f}"
+                    if _skip:
                         progress.console.print(
                             f"    [dim]⏭  {fname}: Frigate score {pre_fscore:.2f}"
-                            f" > ceiling {Config.FRIGATE_SCORE_CEILING:.2f}, already covered[/dim]"
+                            f" > {_bar_str}, already covered[/dim]"
                         )
                         progress.advance(upload_task)
                         continue
@@ -504,65 +489,45 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                     using_fscore = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
                     if using_fscore:
                         candidate_score = pre_fscore
-                        if candidate_score is None:
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: Frigate recognize unavailable, skipping replacement[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        # Low score = more novel than the most redundant mapped file = replace
-                        target = get_most_redundant_mapped_file(name, exclude=failed_deletes)
-                        if target is None or candidate_score >= target[2]:
-                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: frigate {candidate_score:.3f} ≥ most redundant"
-                                f" {target_score_str}, not more novel[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        target_frigate_file, _target_asset_id, target_score = target
-                        progress.console.print(
-                            f"    🔄 {fname}: frigate {candidate_score:.3f} < {target_score:.3f},"
-                            f" replacing {target_frigate_file} (more novel)"
-                        )
-                        if delete_frigate_person_files(name, [target_frigate_file]):
-                            remove_frigate_file(name, target_frigate_file)
-                            person_has_fscores = has_frigate_scores(name)
-                            effective_count -= 1
-                            # clear any blur-mode slot floor — Frigate uses a different score metric
-                            min_quality_score_for_slot = None
-                        else:
-                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
-                            failed_deletes.add(target_frigate_file)
-                            progress.advance(upload_task)
-                            continue
+                        get_target = get_most_redundant_mapped_file
+                        score_label, better_note = "frigate", " (more novel)"
+                        no_score_msg = "Frigate recognize unavailable, skipping replacement"
                     else:
                         candidate_score = score_map.get(fname)
+                        get_target = get_lowest_quality_mapped_file
+                        score_label, better_note = "blur", ""
+                        no_score_msg = "no quality score, skipping replacement"
+
                     if candidate_score is None:
+                        progress.console.print(f"    [dim]⏭  {fname}: {no_score_msg}[/dim]")
+                        progress.advance(upload_task)
+                        continue
+
+                    target = get_target(name, exclude=failed_deletes)
+                    not_better = target is None or (
+                        candidate_score >= target[2] if using_fscore else candidate_score <= target[2]
+                    )
+                    if not_better:
+                        target_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                        op = "<" if using_fscore else ">"
                         progress.console.print(
-                                f"    [dim]⏭  {fname}: no quality score, skipping replacement[/dim]"
-                            )
-                            progress.advance(upload_task)
-                            continue
-                        target = get_lowest_quality_mapped_file(name, exclude=failed_deletes)
-                        if target is None or candidate_score <= target[2]:
-                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
-                            progress.console.print(
-                                f"    [dim]⏭  {fname}: blur {candidate_score:.3f} ≤ worst"
-                                f" {target_score_str}, skipping[/dim]"
+                            f"    [dim]⏭  {fname}: {score_label} {candidate_score:.3f}"
+                            f" not {op} {target_str}, skipping[/dim]"
                         )
                         progress.advance(upload_task)
                         continue
+
                     target_frigate_file, _target_asset_id, target_score = target
+                    op = "<" if using_fscore else ">"
                     progress.console.print(
-                            f"    🔄 {fname}: blur {candidate_score:.3f} > {target_score:.3f},"
-                            f" replacing {target_frigate_file}"
+                        f"    🔄 {fname}: {score_label} {candidate_score:.3f} {op} {target_score:.3f},"
+                        f" replacing {target_frigate_file}{better_note}"
                     )
                     if delete_frigate_person_files(name, [target_frigate_file]):
                         remove_frigate_file(name, target_frigate_file)
                         person_has_fscores = has_frigate_scores(name)
                         effective_count -= 1
-                            min_quality_score_for_slot = score_map.get(fname)
+                        min_quality_score_for_slot = None if using_fscore else candidate_score
                     else:
                         logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
                         failed_deletes.add(target_frigate_file)

winnow/image_processing.py

@@ -1,7 +1,8 @@
-"""Image processing functions for cropping faces and objects."""
+"""Image processing functions for cropping faces."""
 
 import logging
 import os
+import warnings
 
 import numpy as np
 from PIL import Image
@@ -10,9 +11,6 @@ from .config import Config
 
 logger = logging.getLogger(__name__)
 
-# Lazy singleton
-_yolo_model = None
-
 
 def _save_jpeg(img: Image.Image, path: str) -> None:
     if img.mode != "RGB":
@@ -20,17 +18,6 @@ def _save_jpeg(img: Image.Image, path: str) -> None:
     img.save(path, format="JPEG")
 
 
-def get_yolo_model():
-    """Singleton for YOLO model."""
-    global _yolo_model
-    if _yolo_model is None:
-        from ultralytics import YOLO
-
-        logger.info("Loading YOLOv9c model...")
-        _yolo_model = YOLO("yolov9c.pt")
-    return _yolo_model
-
-
 def align_face(img: Image.Image, landmarks: list[list[float]] | np.ndarray) -> Image.Image | None:
     """Align face using 5-point landmarks to standard ArcFace input format (112x112).
 
@@ -127,6 +114,8 @@ def process_face_mode(
                 min(img_h, y2 + pad_y),
             )
             search_crop = img.crop(search_box)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", message=".*estimate.*is deprecated", category=FutureWarning)
                 detected = insightface_app.get(np.asarray(search_crop))
             if detected:
                 cx, cy = search_crop.width / 2, search_crop.height / 2
@@ -170,49 +159,4 @@ def process_face_mode(
     return face_crop.size
 
 
-def process_object_mode(
-    img: Image.Image,
-    config: dict,
-    output_dir: str,
-    count: int,
-) -> bool:
-    """Detect and crop objects using YOLO."""
-    try:
-        model = get_yolo_model()
-        target_class = config.get("object_class", "dog")
-        import torch
-
-        if os.getenv("FORCE_CPU", "").lower() in ("true", "1", "yes"):
-            device = "cpu"
-        elif hasattr(torch, "xpu") and torch.xpu.is_available():
-            device = "xpu"
-        else:
-            device = None  # YOLO auto-selects (CUDA/ROCm/CPU)
-
-        results = model(img, verbose=False, device=device)
-
-        found = False
-        class_idx = 0  # Sequential counter per target class (Issue #10)
-        for box in (box for r in results for box in r.boxes):
-            cls_id = int(box.cls[0])
-            conf = float(box.conf[0])
-            if 0 <= cls_id < len(model.names) and model.names[cls_id] == target_class and conf > 0.5:
-                x1, y1, x2, y2 = box.xyxy[0].tolist()
-                _save_jpeg(
-                    img.crop((x1, y1, x2, y2)),
-                    os.path.join(output_dir, f"{count}_{class_idx}.jpg"),
-                )
-                class_idx += 1
-                found = True
-
-        return found
-    except Exception as e:
-        logger.error(f"YOLO processing failed: {e}")
-        return False
-
-
-def process_full_mode(img: Image.Image, output_dir: str, count: int) -> bool:
-    """Save full image."""
-    _save_jpeg(img, os.path.join(output_dir, f"{count}.jpg"))
-    return True
 

winnow/immich_api.py

@@ -5,7 +5,6 @@ from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
 from io import BytesIO
 
-import numpy as np
 import requests
 from PIL import Image, ImageOps
 
@@ -21,7 +20,6 @@ _MAX_ASSETS_PER_PERSON = 5000  # Stop fetching after this many — diversity poo
 class FaceData:
     """Pre-computed face data from Immich."""
 
-    embedding: np.ndarray | None
     bbox: tuple[float, float, float, float]  # (x1, y1, x2, y2)
     confidence: float | None
     image_width: int
@@ -110,7 +108,7 @@ def fetch_all_assets(person: dict) -> list[dict]:
 
 
 def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | None:
-    """Fetch pre-computed face data (embedding, bbox, confidence) from Immich.
+    """Fetch pre-computed face data (bbox, confidence) from Immich.
 
     Queries GET /api/faces?id={asset_id} to retrieve face detection results
     that Immich already computed using InsightFace Buffalo_L.
@@ -120,7 +118,7 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
         person_id: Optional person ID to match the specific face
 
     Returns:
-        FaceData with embedding, bbox, and confidence, or None if unavailable
+        FaceData with bbox and confidence, or None if unavailable
     """
     try:
         resp = requests.get(
@@ -148,12 +146,6 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
         if face is None:
             face = faces[0]  # Fall back to first/largest face
 
-        # Extract embedding if available
-        embedding = None
-        if "embedding" in face:
-            embedding = np.array(face["embedding"], dtype=np.float32)
-
-        # Extract bounding box
         bbox = (
             face.get("boundingBoxX1", 0),
             face.get("boundingBoxY1", 0),
@@ -163,7 +155,6 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
 
         score = face.get("score")
         return FaceData(
-            embedding=embedding,
             bbox=bbox,
             confidence=score if score is not None else face.get("confidence"),
             image_width=face.get("imageWidth", 0),

winnow/jobs.py

@@ -20,18 +20,16 @@ logger = logging.getLogger(__name__)
 
 # Strategy presets: (limit, mode_name)
 STRATEGY_PRESETS = {
-    "1": ("auto", "Auto Diversity"),
+    "1": ("auto", "Adaptive Diversity"),
     "2": (30, "Standard (30)"),
     "3": (100, "Broad (100)"),
 }
 
 
-def _get_strategy_choice(has_embedding: bool, entity_type: str) -> tuple[int | str, str]:
+def _get_strategy_choice(has_embedding: bool) -> tuple[int | str, str]:
     """Prompt user for training strategy and return (limit, selection_mode)."""
-    model_name = "InsightFace" if entity_type == "face" else "SigLIP"
-
     if has_embedding:
-        rprint("  [bold]1.[/bold] Auto (Objective Diversity) [green][Recommended][/green]")
+        rprint("  [bold]1.[/bold] Adaptive Diversity [green][Recommended][/green]")
         rprint("     [dim]• Dynamically selects images until redundancy starts[/dim]")
         rprint("  [bold]2.[/bold] Standard (30 images)")
         rprint("  [bold]3.[/bold] Broad (100 images)")
@@ -51,7 +49,7 @@ def _get_strategy_choice(has_embedding: bool, entity_type: str) -> tuple[int | s
         return 30, "smart"
 
     # Fallback when embedding model not available
-    rprint(f"  [yellow]Note: {model_name} not available. Using Time Spread.[/yellow]")
+    rprint("  [yellow]Note: InsightFace not available. Using Time Spread.[/yellow]")
     rprint("  [bold]1.[/bold] Standard (30 images) [green][Recommended][/green]")
     rprint("  [bold]2.[/bold] Broad (100 images)")
     rprint("  [bold]3.[/bold] Custom Count")
@@ -77,7 +75,8 @@ def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, st
         return int(custom_limit), "smart"
 
     strategy_map = {
-        "auto": ("auto", "smart"),
+        "adaptive": ("auto", "smart"),
+        "auto": ("auto", "smart"),  # legacy alias for adaptive
         "standard": (30, "smart"),
         "broad": (100, "smart"),
     }
@@ -85,15 +84,13 @@ def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, st
 
 
 def _perform_selection(
-    assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str, person_id: str | None = None
+    assets: list, limit: int | str, name: str, selection_mode: str, person_id: str | None = None
 ) -> list:
     """Run diversity selection with progress display."""
     if selection_mode == "smart":
-        model_display = "InsightFace (face embeddings)" if entity_type == "face" else "SigLIP (visual embeddings)"
-        rprint(f"\n[cyan]Using {model_display} for diversity analysis...[/cyan]")
+        rprint("\n[cyan]Using InsightFace (face embeddings) for diversity analysis...[/cyan]")
 
-        # Pre-load model explicitly (separate from availability check)
-        load_embedding_model(entity_type)
+        load_embedding_model()
 
         with Progress(
             SpinnerColumn(),
@@ -108,7 +105,6 @@ def _perform_selection(
                 limit,
                 name,
                 selection_mode=selection_mode,
-                entity_type=entity_type,
                 person_id=person_id,
                 progress_callback=lambda c, t: progress.update(task, completed=c, total=t),
             )
@@ -119,9 +115,7 @@ def _perform_selection(
 
     rprint(f"\n[cyan]Using time-spread selection for {limit} images...[/cyan]")
     with console.status(f"[bold]Selecting {limit} images evenly distributed over time...[/bold]"):
-        selected = select_diverse_assets(
-            assets, limit, name, selection_mode="time", entity_type=entity_type, person_id=person_id
-        )
+        selected = select_diverse_assets(assets, limit, name, selection_mode="time", person_id=person_id)
     rprint(f"  [green]Selected {len(selected)} images using time spread.[/green]")
     return selected
 
@@ -131,22 +125,12 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
     name = person["name"]
     console.print(f"\nSelected: [bold green]{name}[/bold green]")
 
-    # Select training mode
-    rprint("\n[bold cyan]Training Mode:[/bold cyan]")
-    rprint("  [bold]1.[/bold] Face (Frigate Face Recognition)")
-    rprint("  [bold]2.[/bold] Object (Frigate Object Classification)")
-
-    mode_choice = Prompt.ask("Choice", choices=["1", "2"], default="1")
-    entity_type = "face" if mode_choice == "1" else "object"
-
-    config = {"name": name, "mode": entity_type, "quality_replacement": Config.QUALITY_REPLACEMENT}
-    if entity_type == "object":
-        config["object_class"] = Prompt.ask("Enter Object Class (e.g. dog, cat, car)", default="dog")
+    config = {"name": name, "quality_replacement": Config.QUALITY_REPLACEMENT}
 
     # Fetch and filter assets
     years = IntPrompt.ask("Filter images older than (years)", default=Config.YEARS_FILTER)
 
-    console.print(f"Scanning for {name} ({entity_type})...")
+    console.print(f"Scanning for {name}...")
     with console.status("[bold green]Fetching assets...[/bold green]"):
         all_assets = fetch_all_assets(person)
         recent_assets = filter_recent_assets(all_assets, years=years)
@@ -170,17 +154,15 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
         return None
 
     # Strategy selection
-    has_embedding = is_embedding_available(entity_type)
+    has_embedding = is_embedding_available()
     rprint(f"\n[bold cyan]Select Training Strategy for {name}:[/bold cyan]")
 
-    limit, selection_mode = _get_strategy_choice(has_embedding, entity_type)
+    limit, selection_mode = _get_strategy_choice(has_embedding)
     if selection_mode == "skip":
         return None
 
     # Perform selection
-    selected_assets = _perform_selection(
-        recent_assets, limit, name, selection_mode, entity_type, person_id=person["id"]
-    )
+    selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"])
 
     rprint(f"  [green]Queued {len(selected_assets)} images for {name}.[/green]")
     return {"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config}
@@ -230,7 +212,6 @@ def auto_configure(people: list[dict]) -> list[dict]:
         rprint("[red]No people found with names in Immich.[/red]")
         return []
 
-    mode = os.environ.get("TRAINING_MODE", "face")
     strategy = os.environ.get("STRATEGY", "auto")
     skip = os.environ.get("SKIP_PEOPLE", "").split(",") if os.environ.get("SKIP_PEOPLE") else []
     only = os.environ.get("ONLY_PEOPLE", "").split(",") if os.environ.get("ONLY_PEOPLE") else []
@@ -259,11 +240,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
     jobs = []
     for person in valid_people:
         name = person["name"]
-        entity_type = mode
-
-        config = {"name": name, "mode": entity_type}
-        if entity_type == "object":
-            config["object_class"] = os.environ.get("OBJECT_CLASS", "dog")
+        config = {"name": name}
 
         all_assets = fetch_all_assets(person)
         recent_assets = filter_recent_assets(all_assets, years=Config.YEARS_FILTER)
@@ -306,7 +283,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
 
         config["quality_replacement"] = quality_replacement_only or Config.QUALITY_REPLACEMENT
 
-        has_embedding = is_embedding_available(entity_type)
+        has_embedding = is_embedding_available()
         limit, selection_mode = _resolve_strategy(strategy, has_embedding)
 
         # Cap selection to remaining capacity (no cap when replacement-only — executor
@@ -322,9 +299,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
         if selection_mode == "skip":
             continue
 
-        selected_assets = _perform_selection(
-            recent_assets, limit, name, selection_mode, entity_type, person_id=person["id"]
-        )
+        selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"])
         if auto_cap is not None:
             selected_assets = selected_assets[:auto_cap]
 
@@ -339,20 +314,17 @@ def _show_preview(jobs: list[dict]) -> None:
     """Show a summary table of all queued jobs before execution."""
     table = Table(title="📋 Training Job Preview", show_header=True, header_style="bold cyan")
     table.add_column("Person", style="bold")
-    table.add_column("Mode", style="dim")
     table.add_column("Images", justify="right")
     table.add_column("Date Range", style="dim")
 
     for job in jobs:
         name = job["person"]["name"]
-        mode = job["config"].get("mode", "face")
         count = str(job["limit"])
 
-        # Date range
         dates = sorted(a.get("fileCreatedAt", "")[:10] for a in job["assets"] if a.get("fileCreatedAt"))
         date_range = f"{dates[0]} → {dates[-1]}" if len(dates) >= 2 else (dates[0] if dates else "—")
 
-        table.add_row(name, mode, count, date_range)
+        table.add_row(name, count, date_range)
 
     console.print()
     console.print(table)

winnow/log_config.py

@@ -13,12 +13,9 @@ console = Console()
 NOISY_LOGGERS = (
     "urllib3",
     "PIL",
-    "ultralytics",
     "insightface",
     "onnxruntime",
     "matplotlib",
-    "transformers",
-    "torch",
 )
 
 
@@ -51,7 +48,6 @@ def setup_logging(verbose: bool = False) -> logging.Logger:
 
     # Suppress Python warnings from ML libraries
     warnings.filterwarnings("ignore", category=UserWarning, module="onnxruntime")
-    warnings.filterwarnings("ignore", category=FutureWarning, module="transformers")
 
     return root
 

winnow/upload_tracker.py

@@ -39,6 +39,11 @@ logger = logging.getLogger(__name__)
 UPLOAD_TRACKER_FILE = "frigate_uploaded_ids.json"
 REJECT_TRACKER_FILE = "frigate_rejected_ids.json"
 
+# Write-through in-memory cache keyed by the resolved file path.
+# Reduces per-call JSON reads from O(calls) to O(1) after the first load.
+# Keyed by full path so tests with isolated tmp dirs never share entries.
+_cache: dict[str, dict] = {}
+
 
 def _tracker_path(filename: str) -> Path:
     try:
@@ -50,18 +55,23 @@ def _tracker_path(filename: str) -> Path:
 
 def _load(filename: str) -> dict:
     path = _tracker_path(filename)
-    if not path.exists():
-        return {}
+    key = str(path)
+    if key in _cache:
+        return _cache[key]
+    data: dict = {}
+    if path.exists():
         try:
             with open(path) as f:
-            return json.load(f)
+                data = json.load(f)
         except (json.JSONDecodeError, OSError) as e:
             logger.warning(f"Could not load tracker {filename}: {e}")
-        return {}
+    _cache[key] = data
+    return data
 
 
 def _save(filename: str, data: dict) -> None:
     path = _tracker_path(filename)
+    _cache[str(path)] = data  # keep cache consistent with what we write
     path.parent.mkdir(parents=True, exist_ok=True)
     with open(path, "w") as f:
         json.dump(data, f, indent=2)