chore: remove remaining dead-mode references from executor, jobs, compose, Dockerfile

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

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"]

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) ────────────────────

winnow/executor.py

@@ -173,11 +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
 
@@ -196,8 +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"]
+            person, assets = job["person"], job["assets"]
-            name, mode = person["name"], config.get("mode", "face")
+            name = person["name"]
 
             job_task = progress.add_task(f"Processing {name}...", total=len(assets))
             try:
@@ -206,8 +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 os.path.isdir(person_dir):
-            if mode == "face" and os.path.isdir(person_dir):
                 shutil.rmtree(person_dir)
             os.makedirs(person_dir, exist_ok=True)
 
@@ -219,9 +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
+                    # Enrich the asset with face bounding box data from the Immich
-                    # from the Immich faces API (not included in search/metadata results)
+                    # faces API (not included in search/metadata results).
-                    if mode == "face":
                     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.

winnow/jobs.py

@@ -125,7 +125,7 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
     name = person["name"]
     console.print(f"\nSelected: [bold green]{name}[/bold green]")
 
-    config = {"name": name, "mode": "face", "quality_replacement": Config.QUALITY_REPLACEMENT}
+    config = {"name": name, "quality_replacement": Config.QUALITY_REPLACEMENT}
 
     # Fetch and filter assets
     years = IntPrompt.ask("Filter images older than (years)", default=Config.YEARS_FILTER)
@@ -240,7 +240,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
     jobs = []
     for person in valid_people:
         name = person["name"]
-        config = {"name": name, "mode": "face"}
+        config = {"name": name}
 
         all_assets = fetch_all_assets(person)
         recent_assets = filter_recent_assets(all_assets, years=Config.YEARS_FILTER)