docs: add 0.2.10 changelog

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
fix: audit — score falsy-zero, crop person mismatch, adaptive cap, suppress_output, ldconfig glob, scheduler sleep
2026-06-12 16:51:53 +00:00 · 2026-06-12 16:48:24 +00:00 · 2026-06-12 16:38:40 +00:00 · 2026-06-12 15:58:29 +00:00 · 2026-06-12 15:50:51 +00:00 · 2026-06-12 15:49:28 +00:00
37 changed files with 3740 additions and 1049 deletions
@@ -4,7 +4,10 @@ API_KEY=your-immich-api-key
 FRIGATE_URL=http://192.168.1.10:5000

 # ── Mode & Strategy ───────────────────────────────────────────────────────────
-AUTO_MODE=true
+# Auto mode is active by default when no TTY is present (Docker/cron).
+# Set AUTO_MODE=true to force auto mode even in an interactive terminal.
+# AUTO_MODE=true
+# VERBOSE=true                # Enable DEBUG-level console output (log file is always DEBUG)
 # TRAINING_MODE: face = upload to Frigate face recognition API
 #                object = save crops to output dir for manual Frigate placement
 TRAINING_MODE=face
@@ -29,11 +32,11 @@ STRATEGY=auto
 # MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 80)

 # ── Caching & Models ──────────────────────────────────────────────────────────
-FORCE_CPU=false
-ENABLE_CACHE=true
+# FORCE_CPU=true              # Disable GPU, fall back to CPU
+# ENABLE_CACHE=false          # Disable embedding cache (default: true)
 CACHE_DIR=/app/.if_cache
 HF_HOME=/models/huggingface
-INSIGHTFACE_HOME=/models
+INSIGHTFACE_HOME=/models/.insightface

 # ── Tracker overrides (one-shot — remove after use) ───────────────────────────
 # DRY_RUN=true                # Preview selection without downloading/uploading
@@ -41,5 +44,10 @@ INSIGHTFACE_HOME=/models
 # RESET_PERSON=John           # Clear uploaded+rejected history for one person

 # ── Scheduling ────────────────────────────────────────────────────────────────
-# Cron expression (unset = run once and exit)
-CRON_SCHEDULE=0 3 * * 0     # Every Sunday at 3 AM
+# CRON_SCHEDULE controls container lifetime:
+#   unset          — run once on startup, then exit
+#   empty string   — stay alive, run nothing (trigger manually: docker exec -it winnow winnow)
+#   cron expression — run on startup, then on schedule
+# CRON_SCHEDULE=              # Manual mode (keep alive, no auto-run)
+# CRON_SCHEDULE=0 3 * * 0    # Every Sunday at 3 AM
+# CRON_SCHEDULE=0 3 1 * *    # First of every month
@@ -15,22 +15,8 @@ env:
  IMAGE_NAME: sudolulo/winnow

 jobs:
-  verify-lockfile:
-    name: Verify uv.lock is current
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Check lockfile is up to date
-        run: uv lock --check
-
  build:
    name: Build (${{ matrix.platform }})
-    needs: verify-lockfile
    runs-on: ${{ matrix.runner }}
    strategy:
      matrix:
@@ -53,17 +39,17 @@ jobs:
          echo "Disk space freed."

      - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Set up QEMU
        if: matrix.platform == 'linux/arm64'
        uses: docker/setup-qemu-action@v3

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4

      - name: Log in to GHCR
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
@@ -112,10 +98,10 @@ jobs:
          merge-multiple: true

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4

      - name: Log in to GHCR
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
@@ -141,3 +127,57 @@ jobs:
        run: |
          docker buildx imagetools inspect ${{ steps.tags.outputs.tags }}

+  build-cpu:
+    name: Build CPU-only (amd64)
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Free up disk space
+        run: |
+          sudo rm -rf /usr/share/dotnet
+          sudo rm -rf /opt/ghc
+          sudo rm -rf "/usr/local/share/boost"
+          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
+          echo "Disk space freed."
+
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v4
+
+      - name: Log in to GHCR
+        uses: docker/login-action@v4
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Determine CPU image tag
+        id: cpu-tag
+        run: |
+          if [ "${{ github.ref_name }}" = "dev" ]; then
+            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:dev-cpu" >> "$GITHUB_OUTPUT"
+          else
+            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:cpu" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Build and push CPU image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ./Dockerfile
+          platforms: linux/amd64
+          build-args: VARIANT=cpu
+          cache-from: type=gha,scope=linux/amd64-cpu
+          cache-to: type=gha,mode=max,scope=linux/amd64-cpu
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+          push: true
+          tags: ${{ steps.cpu-tag.outputs.tag }}
+
+      - name: Inspect CPU image
+        run: |
+          docker buildx imagetools inspect ${{ steps.cpu-tag.outputs.tag }}
@@ -9,6 +9,8 @@ on:
 jobs:
  lint:
    runs-on: ubuntu-latest
+    permissions:
+      contents: read
    env:
      FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
    steps:
@@ -21,7 +23,7 @@ jobs:
          echo "Disk space freed."

      - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Run Ruff
        uses: astral-sh/ruff-action@v3
@@ -14,22 +14,8 @@ concurrency:
  cancel-in-progress: true

 jobs:
-  verify-lockfile:
-    name: Verify uv.lock is current
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Check lockfile is up to date
-        run: uv lock --check
-
  release:
    name: Create GitHub Release & Build Image
-    needs: verify-lockfile
    runs-on: ubuntu-latest
    permissions:
      contents: write
@@ -44,15 +30,21 @@ jobs:
          echo "Disk space freed."

      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
        with:
          fetch-depth: 0

+      - name: Install uv
+        uses: astral-sh/setup-uv@v7
+
+      - name: Ensure uv.lock is current
+        run: uv lock
+
      - name: Set up QEMU
        uses: docker/setup-qemu-action@v3

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4

      - name: Resolve tag name
        id: tag
@@ -101,7 +93,7 @@ jobs:
          fi

      - name: Create GitHub Release
-        uses: actions/github-script@v7
+        uses: actions/github-script@v9
        env:
          RELEASE_TAG: ${{ steps.tag.outputs.TAG }}
          RELEASE_NOTES: ${{ steps.changelog.outputs.NOTES }}
@@ -120,7 +112,7 @@ jobs:
            });

      - name: Log in to GHCR
-        uses: docker/login-action@v3
+        uses: docker/login-action@v4
        with:
          registry: ghcr.io
          username: ${{ github.actor }}
@@ -138,4 +130,3 @@ jobs:
          tags: |
            ghcr.io/sudolulo/winnow:latest
            ghcr.io/sudolulo/winnow:${{ steps.tag.outputs.TAG }}
-
@@ -9,15 +9,17 @@ on:
 jobs:
  test:
    runs-on: ubuntu-latest
+    permissions:
+      contents: read
    steps:
      - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Install uv
-        uses: astral-sh/setup-uv@v4
+        uses: astral-sh/setup-uv@v7

      - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.13

      - name: Install dependencies
        run: uv sync --all-extras
@@ -22,13 +22,13 @@ jobs:
          echo "Disk space freed."

      - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6

      - name: Install uv
-        uses: astral-sh/setup-uv@v4
+        uses: astral-sh/setup-uv@v7

      - name: Set up Python
-        run: uv python install 3.12
+        run: uv python install 3.13

      - name: Regenerate lockfile
        run: uv lock
@@ -1,238 +1,36 @@
-# Custom
+# Output and runtime artefacts
 frigate_train/
 *.log
 runs/
+
+# Model and cache files
 yolov9c.pt
 .insightface/
 .huggingface/
-.cache/huggingface
 .if_cache/
-
 .immich_config.json
-# Python-generated files
-__pycache__/
-*.py[oc]
-build/
-dist/
-wheels/
-*.egg-info

-# Virtual environments
-.venv
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[codz]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#   Usually these files are written by a python script from a template
-#   before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py.cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-# Pipfile.lock
-
-# UV
-#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-# uv.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-# poetry.lock
-# poetry.toml
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
-#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
-# pdm.lock
-# pdm.toml
-.pdm-python
-.pdm-build/
-
-# pixi
-#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
-# pixi.lock
-#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
-#   in the .venv directory. It is recommended not to include this directory in version control.
-.pixi
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# Redis
-*.rdb
-*.aof
-*.pid
-
-# RabbitMQ
-mnesia/
-rabbitmq/
-rabbitmq-data/
-
-# ActiveMQ
-activemq-data/
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
+# Secrets
 .env
 .envrc
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/

-# Spyder project settings
-.spyderproject
-.spyproject
+# Python
+__pycache__/
+*.py[oc]
+*.so
+.Python

-# Rope project settings
-.ropeproject
+# Packaging
+build/
+dist/
+*.egg-info/
+wheels/

-# mkdocs documentation
-/site
+# Virtual environments
+.venv/

-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#   and can be added to the global gitignore or merged into this file.  For a more nuclear
-#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
-# .idea/
-
-# Abstra
-#   Abstra is an AI-powered process automation framework.
-#   Ignore directories containing user credentials, local state, and settings.
-#   Learn more at https://abstra.io/docs
-.abstra/
-
-# Visual Studio Code
-#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
-#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
-#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
-#   you could uncomment the following to ignore the entire vscode folder
-# .vscode/
-
-# Ruff stuff:
+# Tools
 .ruff_cache/
-
-# PyPI configuration file
-.pypirc
-
-# Marimo
-marimo/_static/
-marimo/_lsp/
-__marimo__/
-
-# Streamlit
-.streamlit/secrets.toml
+.pytest_cache/
+.mypy_cache/
+.python-version
@@ -1 +0,0 @@
-3.12
@@ -7,6 +7,130 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

 ## [Unreleased]

+## [0.2.10] - 2026-06-12
+
+### Added
+
+- **`VERBOSE` env var**: set `VERBOSE=true` to enable DEBUG-level console output. The log file always captures DEBUG; this flag controls what appears on the terminal. Useful when diagnosing issues without a full shell into the container.
+- **`:cpu` Docker image tag**: a separate CPU-only image (`ghcr.io/sudolulo/winnow:cpu`) is now built and pushed alongside `:latest`. Uses `onnxruntime` instead of `onnxruntime-gpu`; ~2 GB smaller. Suitable for systems without an NVIDIA GPU.
+- **Empty `CRON_SCHEDULE` keeps container alive**: setting `CRON_SCHEDULE=` (empty string) starts the container without running immediately and without exiting — useful for `docker exec` ad-hoc runs on a long-lived container. Previously, an empty value was treated the same as unset (run once, then exit).
+
+### Changed
+
+- **TTY auto-detection replaces `AUTO_MODE`**: winnow now detects whether a TTY is attached (`sys.stdin.isatty()`) and switches between interactive and auto mode automatically. `AUTO_MODE=true` becomes an explicit override for forcing auto mode in a terminal session. No config change needed for normal Docker deployments.
+- **Logging levels audited**: internal algorithmic detail (clustering steps, asset fetch progress, per-page pagination) demoted from INFO to DEBUG. INFO now reflects meaningful pipeline milestones only (model ready, selection complete, quality filtered). Reduces noise in production logs without losing information.
+- **Model load logging improved**: SigLIP and InsightFace loading now reports cache hit/miss, download size estimate, device used, and load time.
+
+### Fixed
+
+- **GPU OOM crash loop (production)**: `CUDAExecutionProvider` was silently absent even with a GPU attached, causing InsightFace to run on CPU and exhaust RAM processing large person libraries. Root cause: CUDA/cuDNN libraries in nvidia pip packages were invisible to onnxruntime. Fixed by running `ldconfig` over all `nvidia-*/lib/` directories in the venv at image build time.
+- **ldconfig path now Python-version-agnostic**: the `find` command used to register nvidia pip libraries hardcoded `python3.13`; replaced with `python3.*` glob so the path survives a Python upgrade without silently producing an empty ldconfig config.
+- **`PYTHONPATH=/app` added to Dockerfile**: the entry point script sets `sys.path[0]` to the script directory, not `/app`. Since `uv sync` runs before `COPY winnow/`, the wheel has only dist-info in site-packages. `PYTHONPATH=/app` makes the `winnow` package importable without reverting to `python -m`.
+- **CPU fallback retrying broken GPU provider**: InsightFace CPU fallback omitted `providers=["CPUExecutionProvider"]`, causing onnxruntime to retry `CUDAExecutionProvider` on every inference call. Now explicitly sets the CPU provider and suppresses C-extension noise via fd-level redirect.
+- **`_suppress_output` stderr loss on fd exhaustion**: if the first `os.dup2` in the finally block raised `OSError`, the second call was skipped, permanently redirecting stderr to `/dev/null` for the process lifetime. Wrapped in nested `try/finally` so both restores are always attempted.
+- **Frigate `/api/faces` response parsing**: the response is `{person_name: [files], "train": [...]}` — `"train"` is a flat pending list, not a person. Previous code called `.items()` on the `"train"` value (a list), crashing with `AttributeError`. Now skips the `"train"` key explicitly.
+- **Immich 401 detection**: a stale or invalid API key now logs a clear error message (`Immich API key is invalid or expired (401 Unauthorized)`) instead of raising an unhandled exception.
+- **Falsy-zero detection confidence**: `face.get("score") or face.get("confidence")` treated a valid `score=0.0` as falsy, falling through to the `confidence` field (often `None`). Replaced with an explicit `None` check. Affected both quality filtering and hard-example weighting in diversity selection.
+- **Face crop using wrong person's image dimensions**: in multi-person assets, `_crop_face_from_thumbnail`'s scale-factor loop matched the first person with any face regardless of `person_id`, producing incorrectly scaled bounding box coordinates for the target person. Loop now applies the same `person_id` filter as `_get_face_bbox`.
+- **Adaptive stopping bypassed for partially-trained people**: in auto mode with `already_uploaded > 0`, `limit` was converted from `"auto"` to an integer, disabling the FPS adaptive threshold and early-stop check. Now keeps `limit="auto"` through selection and trims the result to the remaining capacity afterward.
+- **Embedding cache key mismatch**: HuggingFace cache path check hardcoded the model slug string; replaced with a derivation from `model_name` using `"models--" + model_name.replace("/", "--")` so the check stays correct if the model name changes.
+- **Scheduler: sleep until next run**: the loop slept a fixed 60 seconds regardless of schedule interval, causing runs to fire up to 59 seconds late and waking the process unnecessarily on long schedules (e.g. weekly). Now sleeps exactly until `next_run`.
+- **Scheduler swallowing `SystemExit`**: `except BaseException` in the run wrapper was replaced with `except Exception` (with `KeyboardInterrupt` re-raised above), so `sys.exit()` calls propagate correctly.
+- **Log handler leak**: `setup_logging` now closes and removes existing handlers before adding new ones, preventing file handle accumulation across repeated calls.
+- **`RETRY_REJECTED` silently applied in interactive mode**: the env var was applied unconditionally even in interactive sessions. Now used only as the default for the interactive prompt so users can override it per-run.
+- **`compose.yml` comment inverted**: a comment stated `-it` forces non-interactive mode; corrected to reflect that `-it` allocates a TTY (interactive mode).
+
+### Security
+
+- API key is no longer stored in any config file. All authentication uses environment variables or `.env` only.
+
+## [0.2.9] - 2026-06-12
+
+### Fixed
+
+- **arm64: `onnxruntime-gpu` has no arm64 wheels**: `onnxruntime-gpu` only publishes `manylinux_2_27_x86_64` and `manylinux_2_28_x86_64` wheels — `uv sync` on arm64 failed with exit code 2. Gated `onnxruntime-gpu` behind `sys_platform == 'linux' and platform_machine == 'x86_64'`; arm64 and non-Linux installs now get the CPU `onnxruntime` package instead.
+- **uv lockfile now covers arm64**: Added `required-environments` to `[tool.uv]` so the lockfile is solved for both `linux/x86_64` and `linux/aarch64`, preventing silent resolution gaps for the non-build platform.
+
+## [0.2.8] - 2026-06-12
+
+### Fixed
+
+- **Dockerfile: use `add-apt-repository` with isolated GNUPGHOME**: the `curl | gpg --dearmor` approach was silently failing — gpg exits 0 on bad/empty input, leaving an invalid keyring and causing apt to skip the deadsnakes PPA entirely. Reverted to `add-apt-repository ppa:deadsnakes/ppa` with `GNUPGHOME=$(mktemp -d)` to prevent gpg from touching any pre-existing agent socket (the original QEMU crash cause).
+- **arm64 base: removed `--platform=$BUILDPLATFORM`**: the Ubuntu 24.04 base for arm64 is now pulled for the target platform, so the arm64 image contains real arm64 binaries rather than amd64 binaries in an arm64 manifest.
+
+### Changed
+
+- **License changed from MIT to AGPLv3+**: source and network use of winnow now require derivative works to be open-sourced under the same terms.
+
+## [0.2.7] - 2026-06-12
+
+### Fixed
+
+- **arm64 base reverted to Ubuntu 24.04**: Ubuntu 26.04 ships Python 3.14, not 3.13 — `python3.13` was not locatable in its repos. Reverted arm64 base to `ubuntu:24.04`.
+- **Deadsnakes PPA now uses curl+gpg instead of `add-apt-repository`**: `add-apt-repository` spawns a gpg-agent which crashes under QEMU (arm64 CI). The PPA is now added by fetching the key via `curl` and piping through `gpg --dearmor` — no agent, works on both architectures.
+- **PPA conditional removed**: both amd64 (Ubuntu 22.04 CUDA) and arm64 (Ubuntu 24.04) now go through the same deadsnakes install path, eliminating the per-arch branching and the `ARG TARGETARCH` dependency in RUN commands.
+
+## [0.2.6] - 2026-06-12
+
+### Fixed
+
+- **Dockerfile: `ARG TARGETARCH` re-declared in each stage**: Docker's automatic platform ARGs are only in scope for `FROM` instructions, not `RUN` commands. The `$TARGETARCH` variable in the deadsnakes PPA conditional was silently empty, so the PPA was never added and `python3.13` could not be located on the Ubuntu 22.04 CUDA base. Adding `ARG TARGETARCH` at the top of both the `build` and `runtime` stage bodies fixes the amd64 build.
+
+## [0.2.5] - 2026-06-12
+
+### Changed
+
+- **CUDA base upgraded to 13.3.0**: amd64 base image bumped from `nvidia/cuda:12.9.2-cudnn-runtime-ubuntu22.04` to `nvidia/cuda:13.3.0-cudnn-runtime-ubuntu22.04`. Python packages (torch, onnxruntime-gpu) still target CUDA 12.6 via pip-installed libraries; the base image upgrade requires a host GPU driver that supports CUDA 13+.
+- **`torchvision>=0.27.0` added as explicit dependency**: routed through the `pytorch-cu126` index for linux/x86_64, ensuring it resolves to `0.27.0+cu126` (paired with `torch 2.12.0+cu126`) rather than being pulled from PyPI where the older `0.21.0` wheel would downgrade torch to 2.6.0.
+- **`torch>=2.12.0`** floor raised from 2.6.0 to prevent silent downgrade.
+- **`nvidia-cudnn-cu12>=9.0.0`** floor kept intentionally loose — torch pins an exact cuDNN ABI version (`9.10.2.21`) and must own that constraint.
+
+## [0.2.4] - 2026-06-12
+
+### Changed
+
+- **Python 3.13 across all platforms**: bumped from 3.12 to 3.13. amd64 installs Python 3.13 from the deadsnakes PPA on the Ubuntu 22.04 CUDA base; arm64 uses the Python 3.13 package available natively in Ubuntu 26.04. All confirmed dependencies (`insightface 1.0.1`, `onnxruntime-gpu 1.26.0`, `torch 2.12.0+cu126`) have Python 3.13 wheels.
+- **arm64 base: Ubuntu 26.04**: Python 3.12 was removed from Ubuntu 26.04's default repos; upgrading the base pulls Python 3.13 without a PPA.
+- **`requires-python = ">=3.13"`** and `target-version = "py313"` updated in `pyproject.toml`.
+- **CI updated to Python 3.13**: `test.yml` and `update-lockfile.yml` now install Python 3.13.
+- **`uv.lock` regenerated** for Python 3.13.5.
+
+## [0.2.3] - 2026-06-12
+
+### Fixed
+
+- **Clear-text API key storage**: `config.py` no longer writes `API_KEY` to `.immich_config.json`. The key must come from an environment variable or `.env` file. Interactive mode now prints a tip directing users to `.env`. Resolves CodeQL `py/clear-text-storage-sensitive-data`.
+
+### Changed
+
+- **CI workflow permissions**: `test.yml` and `lint.yml` now declare `permissions: contents: read`, following least-privilege principle and resolving `actions/missing-workflow-permissions` scanner alerts.
+- **CI lockfile race condition**: removed the `verify-lockfile` pre-job from `docker-publish.yml` and `release.yml`. The `update-lockfile.yml` bot maintains the lockfile; the verify step raced against it on the same push event and caused false failures. `release.yml` now runs `uv lock` inline so tag-triggered builds are always self-consistent.
+- **Docs moved to wiki**: `docs/` folder removed from the repository. Setup, Troubleshooting, and FAQ pages are now at the [GitHub wiki](https://github.com/sudolulo/winnow/wiki).
+
+## [0.2.2] - 2026-06-12
+
+### Added
+
+- **Confidence scores in upload tracker**: Immich face confidence scores are now stored per asset in `frigate_uploaded_ids.json` under `by_person[name].scores`. Lays the groundwork for future replacement logic (remove low-confidence uploads when better images are found).
+- **Frigate-authoritative capacity tracking**: At startup, `GET /api/faces` is queried on the Frigate host to retrieve the actual number of trained images per person from the `train` directory (pending/unclassified queue is excluded). This count is stored as `frigate_count` in the tracker JSON so it survives Frigate downtime.
+- **Lifetime cap uses Frigate count**: `MAX_AUTO_IMAGES` is now enforced against Frigate's live training image count rather than the local uploaded-asset tally. Fallback priority: live Frigate API → last cached `frigate_count` in JSON → local uploaded count.
+- **Startup summary shows Frigate count**: Tracker summary at startup now includes the last known Frigate training count per person (e.g. `78 uploaded, 2 rejected, 42 in Frigate`).
+- **`winnow/frigate_api.py`**: new module encapsulating Frigate API helpers; currently exposes `get_frigate_face_counts()`.
+
+### Changed
+
+- `upload_tracker.py`: `by_person` entries migrated from flat list to `{asset_ids, scores, frigate_count}` dict. Old list format is read and migrated transparently on first write.
+- `mark_uploaded()` now accepts an optional `score` keyword argument.
+- `get_person_summary()` now returns `frigate_count` and `scores` fields alongside `uploaded` and `rejected`.
+
+## [0.2.1] - 2026-06-12
+
+### Fixed
+
+- **Container startup reinstalling packages**: `entrypoint.sh` used `uv run`, which performs a sync check on every startup and re-downloaded `ruff` and rebuilt the package each time. Replaced with direct `.venv/bin/python` calls to skip the sync entirely.
+- **InsightFace double `models/` path**: `INSIGHTFACE_HOME=/models` caused InsightFace to download Buffalo_L to `/models/models/buffalo_l` (InsightFace always appends `models/` to the root). Updated default in `compose.yml` and `.env.example` to `/models/.insightface`.
+- **Lint errors in CI**: unused imports in `tests/test_config.py` and `tests/test_upload_tracker.py`, unsorted imports in `scheduler.py` — all would have failed the ruff CI check.
+
 ## [0.2.0] - 2026-06-12

 First release of winnow. Forked from [if-curator](https://github.com/ds-sebastian/if_curator) by Sebastian and rewritten for headless Docker deployment.
@@ -21,14 +145,14 @@ First release of winnow. Forked from [if-curator](https://github.com/ds-sebastia

 **Docker and scheduling**
 - `Dockerfile` — multi-stage build (CUDA 12.9 on amd64, plain Ubuntu on arm64); runtime stage excludes build tools (g++, python3.12-dev, curl, gnupg)
- `compose.yml` — fully annotated with inline comments grouped by concern; TrueNAS volume paths in the example
+- `compose.yml` — fully annotated with inline comments grouped by concern
 - `entrypoint.sh` — runs the tool once on startup, then hands off to the scheduler if `CRON_SCHEDULE` is set
 - `scheduler.py` — in-process cron scheduler that keeps the container (and loaded models) alive between runs
 - `CRON_SCHEDULE` env var — standard cron expression for recurring runs; unset exits after first run
 - `.dockerignore` — keeps `.venv`, `__pycache__`, test files, and logs out of the image context
 - Multi-arch image: `linux/amd64` and `linux/arm64` built and merged into a single manifest on GHCR
 - `tini` as PID 1 init process for correct signal handling
- Non-root container user (`appuser`, uid 568) matching TrueNAS default app UID
+- Non-root container user (`appuser`, uid 568)
 - `HEALTHCHECK` in Dockerfile

 **Object mode**
@@ -73,7 +197,7 @@ First release of winnow. Forked from [if-curator](https://github.com/ds-sebastia
 - 24 unit tests across four modules: `test_config`, `test_immich_api`, `test_jobs`, `test_upload_tracker`

 **Documentation**
- `docs/setup.md` — step-by-step install guide with TrueNAS and bare-Docker sections
+- `docs/setup.md` — step-by-step install and GPU passthrough guide
 - `docs/troubleshooting.md` — common failure modes with fixes
 - `docs/faq.md` — answers to questions new users will ask
 - `.env.example` — copy-paste starting point with every env var and inline comments
@@ -1,75 +1,93 @@
-# ── Platform-conditional base ─────────────────────────────────────────────
-#   amd64:  NVIDIA CUDA 12.6 (GPU acceleration when available, CPU fallback)
-#   arm64:  Plain Ubuntu (CPU-only, no CUDA on ARM)
+# ── Base images ───────────────────────────────────────────────────────────────
+#   amd64 + gpu:  NVIDIA CUDA 13.3 + cuDNN (GPU acceleration when available)
+#   amd64 + cpu:  Ubuntu 22.04 (CPU-only, ~2 GB smaller image)
+#   arm64:        Ubuntu 24.04 (CPU-only; no CUDA wheels on ARM)

-FROM --platform=$BUILDPLATFORM nvidia/cuda:12.9.2-cudnn-runtime-ubuntu22.04 AS base-amd64
-FROM --platform=$BUILDPLATFORM ubuntu:22.04 AS base-arm64
+ARG VARIANT=gpu

-# ── Build stage ───────────────────────────────────────────────────────────
+FROM --platform=$BUILDPLATFORM nvidia/cuda:13.3.0-cudnn-runtime-ubuntu22.04 AS base-amd64-gpu
+FROM ubuntu:22.04 AS base-amd64-cpu
+FROM ubuntu:24.04 AS base-arm64-gpu
+FROM ubuntu:24.04 AS base-arm64-cpu
+
+# ── Build stage ───────────────────────────────────────────────────────────────
 ARG TARGETARCH

-FROM base-${TARGETARCH} AS build
+FROM base-${TARGETARCH}-${VARIANT} AS build

+ARG VARIANT=gpu
 ENV DEBIAN_FRONTEND=noninteractive

+# Both Ubuntu 22.04 and 24.04 get Python 3.13 from the deadsnakes PPA.
+# GNUPGHOME is isolated so gpg never contacts an agent socket under QEMU.
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    ca-certificates \
-    curl \
-    gnupg \
-    software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa -y \
-    && apt-get update && apt-get install -y --no-install-recommends \
-    python3.12 python3.12-venv python3.12-dev \
+    ca-certificates curl gnupg software-properties-common \
+    && GNUPGHOME=$(mktemp -d) add-apt-repository ppa:deadsnakes/ppa -y \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends \
+    python3.13 python3.13-venv python3.13-dev \
    libgl1 libglib2.0-0 libxext6 g++ \
    && rm -rf /var/lib/apt/lists/* \
-    && ln -sf /usr/bin/python3.12 /usr/bin/python3
+    && ln -sf /usr/bin/python3.13 /usr/bin/python3

 RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
    && cp /root/.local/bin/uv /usr/local/bin/uv

 WORKDIR /app

-COPY pyproject.toml uv.lock ./
-RUN uv sync --frozen --no-dev \
+# For cpu variant, swap in the CPU-only pyproject and lockfile before syncing.
+COPY pyproject.toml uv.lock pyproject-cpu.toml uv-cpu.lock ./
+RUN if [ "$VARIANT" = "cpu" ]; then \
+        cp pyproject-cpu.toml pyproject.toml && \
+        cp uv-cpu.lock uv.lock; \
+    fi && \
+    uv sync --frozen --no-dev \
    && uv cache clean

 COPY winnow/ winnow/
 COPY entrypoint.sh scheduler.py ./
 RUN chmod +x /app/entrypoint.sh

-# ── Runtime stage ─────────────────────────────────────────────────────────
+# ── Runtime stage ─────────────────────────────────────────────────────────────
 #   Starts fresh from the base image — excludes build tools (g++,
-#   python3.12-dev, curl, gnupg) that are not needed at runtime.
+#   python3.13-dev, gnupg, software-properties-common) not needed at runtime.

-FROM base-${TARGETARCH} AS runtime
+FROM base-${TARGETARCH}-${VARIANT} AS runtime

+ARG VARIANT=gpu
 ENV DEBIAN_FRONTEND=noninteractive

 RUN apt-get update && apt-get install -y --no-install-recommends \
-    ca-certificates \
-    software-properties-common \
-    tini \
-    && add-apt-repository ppa:deadsnakes/ppa -y \
-    && apt-get update && apt-get install -y --no-install-recommends \
-    python3.12 python3.12-venv \
+    ca-certificates curl gnupg software-properties-common tini \
+    && GNUPGHOME=$(mktemp -d) add-apt-repository ppa:deadsnakes/ppa -y \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends \
+    python3.13 python3.13-venv \
    libgl1 libglib2.0-0 libxext6 \
+    && apt-get purge -y --auto-remove curl gnupg software-properties-common \
    && rm -rf /var/lib/apt/lists/* \
-    && ln -sf /usr/bin/python3.12 /usr/bin/python3
+    && ln -sf /usr/bin/python3.13 /usr/bin/python3

 # Copy app (with .venv) and uv from build stage
 COPY --from=build /app /app
 COPY --from=build /usr/local/bin/uv /usr/local/bin/uv

-# Expose CUDA/cuDNN libraries from pip packages so onnxruntime-gpu
-# can find libcublasLt.so.12 and libcudnn.so.9 at runtime
-ENV LD_LIBRARY_PATH="/app/.venv/lib/python3.12/site-packages/nvidia/cudnn/lib:/app/.venv/lib/python3.12/site-packages/nvidia/cuda_runtime/lib:${LD_LIBRARY_PATH}"
+# Register every nvidia pip-package lib/ directory with ldconfig so that
+# onnxruntime-gpu and torch can find libcudnn, libcublas, libcufft, etc.
+# without a hand-maintained LD_LIBRARY_PATH. Skipped silently on cpu builds.
+RUN find /app/.venv/lib/python3.*/site-packages/nvidia -type d -name "lib" \
+        2>/dev/null > /etc/ld.so.conf.d/nvidia-pip.conf && ldconfig || true

 RUN groupadd -g 568 apps && useradd -u 568 -g apps -m -s /bin/bash appuser \
    && mkdir -p /models/.insightface /models/huggingface \
    && chown -R appuser:apps /app /models

+WORKDIR /app
 USER appuser
-ENV HF_HOME=/models/huggingface INSIGHTFACE_HOME=/models
+# 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

 HEALTHCHECK CMD test -f /app/entrypoint.sh || exit 1
 ENTRYPOINT ["tini", "--", "/app/entrypoint.sh"]
@@ -1,22 +1,661 @@
-MIT License
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007

-Copyright (c) 2026 Holden Salomon
-Copyright (c) 2026 Sebastian
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.

-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+                            Preamble

-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.

-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
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+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
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@@ -1,9 +1,9 @@
-[![Publish Docker Image](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml) [![Release](https://github.com/sudolulo/winnow/actions/workflows/release.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/release.yml) [![Lint](https://github.com/sudolulo/winnow/actions/workflows/lint.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/lint.yml) [![Test](https://github.com/sudolulo/winnow/actions/workflows/test.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/test.yml)
-
-**Docs:** [Setup Guide](docs/setup.md) · [Troubleshooting](docs/troubleshooting.md) · [FAQ](docs/faq.md)
-
 # winnow

+[![Docker](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml) [![Test](https://github.com/sudolulo/winnow/actions/workflows/test.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/test.yml) [![Immich](https://img.shields.io/badge/Immich-v1.106%2B-blueviolet)](https://immich.app) [![Frigate](https://img.shields.io/badge/Frigate-Ready-brightgreen)](https://frigate.video)
+
+**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 of people and objects 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.

 It runs fully headless in Docker, is configured entirely through environment variables, and can run on a schedule — no interactive prompts, no manual steps.
@@ -16,6 +16,8 @@ Frigate's face recognition model (ArcFace) and object classifier are only as goo

 If you upload 100 photos from the same week, the model learns the lighting in your living room and the jacket you wore that month. It struggles the moment anything changes. What you actually want is a spread: different years, different lighting conditions, different angles, different contexts.

+This is especially true for people who have never been to your property, or who visit rarely — family members, friends, anyone Frigate has never seen in person. Live detections alone will never build a reliable model for these people. Your photo library already has the data; winnow finds and delivers the right subset of it.
+
 Finding that spread manually across a library of thousands of photos is not practical. `winnow` does it automatically.

 ---
@@ -68,6 +70,14 @@ Uploaded asset IDs are recorded so the same image is never uploaded twice, even

 ---

+## Note on Crop Quality
+
+winnow works well, but no automated pipeline is perfect. Occasionally a bad crop will slip through quality filtering — a partial face, someone in the background, a blurry frame. After a run it's worth a quick review in Frigate's face management UI to remove anything that doesn't belong.
+
+Issues and feedback welcome via [GitHub Issues](https://github.com/sudolulo/winnow/issues).
+
+---
+
 ## Modes

 ### Face Mode (default)
@@ -101,17 +111,23 @@ If the embedding model is unavailable, the tool falls back to **time spread**: e

 ## Running in Docker

+### Image Tags
+
+| Tag | Arch | GPU | Notes |
+| :-- | :-- | :-- | :-- |
+| `:latest` | amd64 + arm64 | CUDA 13.3 (amd64) | Requires NVIDIA Container Toolkit on amd64 |
+| `:cpu` | amd64 | None | ~2 GB smaller; use if you have no NVIDIA GPU |
+
 ### Quick Start

 ```yaml
 services:
  winnow:
-    image: ghcr.io/sudolulo/winnow:latest
+    image: ghcr.io/sudolulo/winnow:latest   # or :cpu for CPU-only amd64
    environment:
      - IMMICH_URL=http://192.168.1.10:2283
      - API_KEY=your-immich-api-key
      - FRIGATE_URL=http://192.168.1.10:5000
-      - AUTO_MODE=true
      - CRON_SCHEDULE=0 3 * * 0   # Every Sunday at 3 AM
    volumes:
      - /path/to/models:/models
@@ -126,11 +142,21 @@ services:
              capabilities: [gpu]
 ```

-See [compose.yml](compose.yml) for the full annotated example including TrueNAS volume paths.
+> **CPU users (`:cpu` tag):** remove the `deploy.resources` block — no NVIDIA runtime needed.
+
+See [compose.yml](compose.yml) for the full annotated example.

 ### Scheduling Behaviour

-On startup the container always runs once immediately. If `CRON_SCHEDULE` is set, it then starts a scheduler that fires on the defined interval, keeping the process (and loaded models) alive between runs. Without `CRON_SCHEDULE` the container exits after the first run.
+`CRON_SCHEDULE` controls container lifetime:
+
+| `CRON_SCHEDULE` value | Behaviour |
+| :-- | :-- |
+| *(unset)* | Run once on startup, then exit |
+| *(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. In manual mode the container idles indefinitely with `sleep infinity` — useful when you want to trigger runs interactively on demand without pulling a new container each time.

 The first run after a fresh install downloads the embedding models (~1-2 GB). Subsequent runs use the cached models from the mounted volume and start immediately.

@@ -142,7 +168,8 @@ The first run after a fresh install downloads the embedding models (~1-2 GB). Su

 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `AUTO_MODE` | `false` | Run without interactive prompts — required for Docker/cron use |
+| `AUTO_MODE` | *(auto)* | Force non-interactive mode even in a terminal; auto-detected otherwise (no TTY = auto) |
+| `VERBOSE` | `false` | Set to `true` to enable DEBUG-level console output (the log file is always DEBUG) |
 | `TRAINING_MODE` | `face` | `face` — upload crops to Frigate API; `object` — save crops to disk |
 | `STRATEGY` | `auto` | `auto` (adaptive), `standard` (30 images), `broad` (100 images) |
 | `LIMIT` | *(unset)* | Exact image count — overrides `STRATEGY` |
@@ -182,7 +209,7 @@ The first run after a fresh install downloads the embedding models (~1-2 GB). Su
 | Variable | Default | Description |
 | :--- | :--- | :--- |
 | `FORCE_CPU` | `false` | Disable GPU — fall back to CPU for embedding computation |
-| `ENABLE_CACHE` | `false` | Cache computed embeddings to disk (speeds up re-runs on the same library) |
+| `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 location (SigLIP) |
 | `INSIGHTFACE_HOME` | *(system)* | InsightFace model cache location (Buffalo_L) |
@@ -199,7 +226,7 @@ The first run after a fresh install downloads the embedding models (~1-2 GB). Su

 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `CRON_SCHEDULE` | *(unset)* | Cron expression for recurring runs — unset exits after first run |
+| `CRON_SCHEDULE` | *(unset)* | Unset = run once and exit; empty = stay alive for manual `docker exec`; cron expression = scheduled |

 ---

@@ -212,7 +239,7 @@ uv sync
 uv run winnow
 ```

-Requires Python 3.12+ and [uv](https://astral.sh/uv/). An NVIDIA GPU is strongly recommended — CPU mode works but embedding computation is significantly slower.
+Requires Python 3.13+ and [uv](https://astral.sh/uv/). An NVIDIA GPU is strongly recommended — CPU mode works but embedding computation is significantly slower.

 ---

@@ -221,10 +248,10 @@ Requires Python 3.12+ and [uv](https://astral.sh/uv/). An NVIDIA GPU is strongly
 - **Immich** v1.106+
 - **Frigate** v0.16+ (face mode only — object mode has no Frigate API dependency)
 - **NVIDIA GPU** recommended (CUDA 12.x)
- **Python 3.12+**
+- **Python 3.13+**

 ---

 ## Attribution

-Based on [winnow](https://github.com/ds-sebastian/if_curator) by Sebastian, licensed MIT.
+Based on [if_curator](https://github.com/ds-sebastian/if_curator) by Sebastian, licensed MIT.
@@ -1,8 +0,0 @@
-services:
-  winnow:
-    build: .
-    network_mode: host
-    volumes:
-      - /code/winnow/models:/models
-      - /code/winnow/embeddings:/app/.if_cache
-      - /code/winnow/output:/app/frigate_train
@@ -9,7 +9,10 @@ services:
      - FRIGATE_URL=${FRIGATE_URL}

      # ── Mode & Strategy ───────────────────────────────────────────────────
-      - AUTO_MODE=true
+      # Auto mode is active by default when no TTY is present (Docker/cron).
+      # 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
@@ -34,11 +37,11 @@ services:
      # - MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 80)

      # ── Caching & Models ──────────────────────────────────────────────────
-      - FORCE_CPU=false
-      - ENABLE_CACHE=true
+      # - FORCE_CPU=true              # Disable GPU, fall back to CPU
+      # - ENABLE_CACHE=false          # Disable embedding cache (default: true)
      - CACHE_DIR=/app/.if_cache
      - HF_HOME=/models/huggingface
-      - INSIGHTFACE_HOME=/models
+      - INSIGHTFACE_HOME=/models/.insightface

      # ── Tracker overrides (one-shot, remove after use) ────────────────────
      # - DRY_RUN=true                # Preview selection without downloading/uploading
@@ -46,18 +49,19 @@ services:
      # - RESET_PERSON=John           # Clear uploaded+rejected history for one person

      # ── Scheduling ────────────────────────────────────────────────────────
-      # Cron expression (unset = run once and exit)
-      # Every Sunday at 3 AM:
-      - CRON_SCHEDULE=0 3 * * 0
-      # - CRON_SCHEDULE=0 3 1 * *
-      # - CRON_SCHEDULE=*/30 * * * *
+      # CRON_SCHEDULE controls container lifetime:
+      #   unset          — run once on startup, then exit
+      #   empty string   — stay alive, run nothing; trigger manually with:
+      #                    docker exec -it winnow winnow
+      #   cron expression — run on startup, then on schedule
+      # - CRON_SCHEDULE=                # Manual mode (keep alive, no auto-run)
+      # - CRON_SCHEDULE=0 3 * * 0      # Every Sunday at 3 AM
+      # - CRON_SCHEDULE=0 3 1 * *      # First of every month
    volumes:
-      # Replace <pool> with your TrueNAS pool name, e.g. /mnt/tank/winnow/...
-      - /mnt/<pool>/winnow/models:/models
-      - /mnt/<pool>/winnow/embeddings:/app/.if_cache
-      - /mnt/<pool>/winnow/output:/app/frigate_train
-    stdin_open: true
-    tty: true
+      # Replace with absolute paths on your host, e.g. /opt/winnow/models
+      - /path/to/winnow/models:/models
+      - /path/to/winnow/cache:/app/.if_cache
+      - /path/to/winnow/output:/app/frigate_train
    restart: unless-stopped
    deploy:
      resources:
@@ -1,63 +0,0 @@
-# FAQ
-
-## Does winnow modify my Immich library?
-
-No. winnow only reads from Immich (assets, people, face bounding boxes). It never writes back to Immich or deletes anything.
-
---
-
-## How many images should I upload to Frigate?
-
-The `auto` strategy decides this for you — it keeps selecting until adding more images would be redundant. In practice this is usually 20–60 per person. You can cap it with `MAX_AUTO_IMAGES` (default 80).
-
-Quality and diversity matter far more than volume. 30 well-spread images outperform 200 from the same week.
-
---
-
-## What's the difference between face mode and object mode?
-
- **Face mode**: Extracts and aligns face crops, uploads them directly to Frigate's face training API. This is for teaching Frigate to recognize specific people.
- **Object mode**: Runs YOLO detection on full images and saves crops of a target class (dog, cat, car, etc.) to disk. Frigate has no API for object training data, so you place them manually.
-
---
-
-## Can I run it without Frigate?
-
-Yes — in object mode, `FRIGATE_URL` is not used and crops are saved to the output volume. In face mode you need Frigate to receive the uploads, but you can use `DRY_RUN=true` to preview selection without uploading.
-
---
-
-## How does auto-diversity mode work?
-
-winnow computes a vector embedding for each candidate image (what the face/object actually looks like — angle, lighting, expression). It then clusters those embeddings and picks representatives that are maximally spread across the embedding space. It stops when the next-most-different image is already close to something already selected. See the README for the full pipeline.
-
---
-
-## Does it support multiple people in one run?
-
-Yes. By default it processes every named person in your Immich library. Use `ONLY_PEOPLE` to whitelist specific names or `SKIP_PEOPLE` to exclude them.
-
---
-
-## What GPU is needed?
-
-Any NVIDIA GPU with CUDA 12.x support. The models (InsightFace Buffalo_L + SigLIP) fit comfortably in 4 GB VRAM. CPU mode works but is significantly slower.
-
-ARM builds (linux/arm64) use CPU-only — CUDA is not available on ARM.
-
---
-
-## Does it work on Unraid / Proxmox / bare Docker?
-
-Yes — the `compose.yml` uses standard Docker volume mounts. The TrueNAS paths in the example (`/mnt/<pool>/...`) are just an example; replace them with whatever paths suit your setup.
-
---
-
-## How do I update winnow?
-
-```bash
-docker compose pull
-docker compose up -d
-```
-
-The `latest` tag on GHCR tracks the `main` branch. Pinning to a version tag (e.g. `ghcr.io/sudolulo/winnow:v0.2.0`) is recommended for stability.
@@ -1,88 +0,0 @@
-# Setup Guide
-
-## Prerequisites
-
- [Immich](https://immich.app) v1.106+ with face recognition enabled and people tagged
- [Frigate](https://frigate.video) v0.16+ (face mode only)
- Docker with the NVIDIA container toolkit (optional but strongly recommended)
-
---
-
-## 1. Get your Immich API key
-
-1. Open Immich → **Account Settings** → **API Keys**
-2. Click **New API Key**, give it a name (e.g. `winnow`), copy the key
-
---
-
-## 2. Get your Frigate URL
-
-This is the base URL of your Frigate instance, e.g. `http://192.168.1.10:5000`. Only needed for face mode — omit it entirely if you're using object mode.
-
---
-
-## 3. Deploy with Docker Compose
-
-Copy [`compose.yml`](../compose.yml) and [`.env.example`](../.env.example) to a directory on your host:
-
-```bash
-mkdir winnow && cd winnow
-curl -O https://raw.githubusercontent.com/sudolulo/winnow/main/compose.yml
-curl -O https://raw.githubusercontent.com/sudolulo/winnow/main/.env.example
-cp .env.example .env
-```
-
-Edit `.env` with your values:
-
-```bash
-IMMICH_URL=http://192.168.1.10:2283
-API_KEY=your-immich-api-key
-FRIGATE_URL=http://192.168.1.10:5000
-```
-
-Edit the volume paths in `compose.yml` to match your storage layout (replace `/mnt/<pool>` with your actual path).
-
-Start it:
-
-```bash
-docker compose up -d
-```
-
-Logs:
-
-```bash
-docker compose logs -f winnow
-```
-
---
-
-## 4. First run
-
-On the first run, winnow downloads the embedding models (~1–2 GB) from HuggingFace and InsightFace. This happens once — subsequent runs use the cached models from your mounted volume and start immediately.
-
---
-
-## 5. Scheduling
-
-Set `CRON_SCHEDULE` in your `.env` to keep winnow running on a schedule:
-
-```
-CRON_SCHEDULE=0 3 * * 0   # Every Sunday at 3 AM
-```
-
-Without `CRON_SCHEDULE`, the container runs once and exits.
-
---
-
-## TrueNAS Scale
-
-The included `compose.yml` uses TrueNAS-style volume paths. Replace `<pool>` with your pool name:
-
-```yaml
-volumes:
-  - /mnt/tank/winnow/models:/models
-  - /mnt/tank/winnow/embeddings:/app/.if_cache
-  - /mnt/tank/winnow/output:/app/frigate_train
-```
-
-GPU passthrough on TrueNAS requires the NVIDIA app to be installed from the TrueNAS catalog and the `deploy.resources.reservations.devices` block in `compose.yml` (already included).
@@ -1,77 +0,0 @@
-# Troubleshooting
-
-## Container exits immediately
-
-Check logs:
-```bash
-docker compose logs winnow
-```
-
-Common causes:
- **Missing required env var** — `IMMICH_URL` or `API_KEY` not set
- **Cannot reach Immich** — check the URL and that Immich is running; use `http://` not `https://` unless you have TLS set up
-
---
-
-## "No people found" / nothing processed
-
- Make sure Immich has completed face recognition and you have named people in your library
- `YEARS_FILTER` defaults to 10 years — increase it if your tagged photos are older
- `MIN_FACE_COUNT` skips people with few photos — lower or remove it
-
---
-
-## Frigate upload fails
-
- Confirm `FRIGATE_URL` is reachable from inside the container: `docker exec winnow curl $FRIGATE_URL/api/stats`
- Check Frigate v0.16+ — older versions don't have the face training API
- Set `DRY_RUN=true` to verify selection without uploading
-
---
-
-## Models fail to download
-
-winnow downloads InsightFace and HuggingFace (SigLIP) models on first run.
-
- Ensure the container has internet access
- Confirm the model volume is mounted and writable
- If behind a proxy, set `HTTP_PROXY` / `HTTPS_PROXY` env vars
-
---
-
-## Running on CPU (no GPU)
-
-Set `FORCE_CPU=true`. Everything works but embedding computation is slower — expect several minutes per person instead of seconds.
-
-If you have a GPU but it's not being used:
- Confirm the NVIDIA container toolkit is installed: `docker run --rm --gpus all nvidia/cuda:12.9.2-base-ubuntu22.04 nvidia-smi`
- Confirm the `deploy.resources.reservations.devices` block is present in `compose.yml`
-
---
-
-## Same images uploaded every run
-
-The upload tracker is stored in `CACHE_DIR` (`/app/.if_cache` by default). If this volume isn't persisted between runs, the tracker resets and images are re-uploaded.
-
-Make sure `/app/.if_cache` is mounted to a persistent host path.
-
---
-
-## Re-uploading a specific person
-
-To clear the upload history for one person and start fresh:
-
-```env
-RESET_PERSON=John
-```
-
-Remove this after one run — it clears the history and then processes normally.
-
---
-
-## Image quality issues
-
- **Too blurry**: Lower `BLUR_THRESHOLD` (default 100) — e.g. `50` accepts more blur
- **Face too small**: Lower `MIN_FACE_WIDTH` (default 50px)
- **Low confidence detections included**: Raise `MIN_CONFIDENCE` (default 0.7)
- **Rejected images being re-tried**: Set `RETRY_REJECTED=true` for one run
@@ -2,16 +2,23 @@
 set -e
 export PYTHONUNBUFFERED=1

-# 1. Run the job immediately on startup
+# CRON_SCHEDULE controls container lifetime:
+#   unset          — run once and exit
+#   empty string   — stay alive, run nothing (use: docker exec -it winnow winnow)
+#   cron expression — run immediately, then on schedule
+
+if [ "${CRON_SCHEDULE+isset}" = "isset" ] && [ -z "$CRON_SCHEDULE" ]; then
+    echo "▶ CRON_SCHEDULE is empty — manual mode. Use 'docker exec -it winnow winnow' to run."
+    exec sleep infinity
+fi
+
 echo "▶ Running on startup..."
-uv run python -m winnow.cli
+/app/.venv/bin/winnow

-# 2. If a schedule exists, start the scheduler
 if [ -n "${CRON_SCHEDULE:-}" ]; then
    echo "▶ CRON_SCHEDULE set to: $CRON_SCHEDULE"
    echo "▶ Switching to scheduled mode..."
-    exec uv run python3 /app/scheduler.py
+    exec /app/.venv/bin/python /app/scheduler.py
 else
    echo "▶ No schedule set, exiting."
 fi
-
@@ -0,0 +1,93 @@
+[project]
+name = "winnow"
+version = "0.2.10"
+description = "Immich to Frigate training sets"
+license = "AGPL-3.0-or-later"
+requires-python = ">=3.13"
+authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }]
+keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Scientific/Engineering :: Image Recognition",
+]
+dependencies = [
+    "croniter>=5.0.2",
+    "insightface>=0.7.3",
+    "numpy>=2.2.6",
+    "onnxruntime>=1.23.2",
+    "opencv-python-headless>=4.12.0.88",
+    "pillow>=12.1.0",
+    "python-dotenv>=1.2.1",
+    "requests>=2.32.5",
+    "rich>=14.2.0",
+    "torch>=2.12.0",
+    "torchvision>=0.27.0",
+    "transformers>=4.57.6",
+    "ultralytics>=8.4.66",
+]
+
+[project.scripts]
+winnow = "winnow.cli:main"
+
+[project.urls]
+Repository = "https://github.com/sudolulo/winnow"
+
+[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",
+    "ruff>=0.15.17",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["winnow"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py313"
+
+[tool.ruff.lint]
+select = ["E", "F", "I"]
+
+[tool.deptry]
+pep621_dev_dependency_groups = ["dev"]
+
+[tool.deptry.package_module_name_map]
+pillow = "PIL"
+opencv-python-headless = "cv2"
+python-dotenv = "dotenv"
+insightface = "insightface"
+numpy = "numpy"
+onnxruntime = "onnxruntime"
+requests = "requests"
+rich = "rich"
+torch = "torch"
+transformers = "transformers"
+ultralytics = "ultralytics"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
@@ -1,16 +1,16 @@
 [project]
 name = "winnow"
-version = "0.2.0"
+version = "0.2.10"
 description = "Immich to Frigate training sets"
-license = "MIT"
-requires-python = ">=3.12"
+license = "AGPL-3.0-or-later"
+requires-python = ">=3.13"
 authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }]
 keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"]
 classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Developers",
-    "License :: OSI Approved :: MIT License",
-    "Programming Language :: Python :: 3.12",
+    "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
+    "Programming Language :: Python :: 3.13",
    "Topic :: Scientific/Engineering :: Image Recognition",
 ]
 dependencies = [
@@ -18,15 +18,18 @@ dependencies = [
    "insightface>=0.7.3",
    "nvidia-cudnn-cu12>=9.0.0",
    "numpy>=2.2.6",
-    "onnxruntime-gpu>=1.23.2",
+    "onnxruntime-gpu>=1.23.2; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "onnxruntime>=1.23.2; sys_platform == 'linux' and platform_machine != 'x86_64'",
+    "onnxruntime>=1.23.2; sys_platform != 'linux'",
    "opencv-python-headless>=4.12.0.88",
    "pillow>=12.1.0",
    "python-dotenv>=1.2.1",
    "requests>=2.32.5",
    "rich>=14.2.0",
-    "torch>=2.6.0",
+    "torch>=2.12.0",
+    "torchvision>=0.27.0",
    "transformers>=4.57.6",
-    "ultralytics>=8.3.252",
+    "ultralytics>=8.4.66",
 ]

 [project.scripts]
@@ -36,7 +39,11 @@ winnow = "winnow.cli:main"
 Repository = "https://github.com/sudolulo/winnow"

 [tool.uv]
-override-dependencies = ["onnxruntime-gpu>=1.23.2"]
+override-dependencies = ["onnxruntime>=1.23.2"]
+required-environments = [
+    "sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "sys_platform == 'linux' and platform_machine == 'aarch64'",
+]

 [tool.uv.sources]
 torch = [
@@ -44,6 +51,11 @@ torch = [
    { 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"
@@ -58,7 +70,7 @@ explicit = true
 [dependency-groups]
 dev = [
    "pytest>=8.0",
-    "ruff>=0.9.2",
+    "ruff>=0.15.17",
 ]

 [tool.hatch.build.targets.wheel]
@@ -66,7 +78,7 @@ packages = ["winnow"]

 [tool.ruff]
 line-length = 120
-target-version = "py312"
+target-version = "py313"

 [tool.ruff.lint]
 select = ["E", "F", "I"]
@@ -1,41 +1,34 @@
 #!/usr/bin/env python3
+import logging
 import os
 import sys
-import subprocess
 import time
-import logging
 from pathlib import Path

 try:
    from croniter import croniter
 except ImportError:
-    print("❌ croniter not installed. Run: uv add croniter")
+    print("croniter not installed. Run: uv add croniter")
    sys.exit(1)

+# Imported at module level so models loaded during the first run stay
+# resident in memory across all subsequent scheduled runs.
+from winnow.cli import main
+
 SCHEDULE = os.environ["CRON_SCHEDULE"]
 MODELS_DIR = os.environ.get("HF_HOME", "/models/huggingface")
-INSIGHTFACE_BASE = os.environ.get("INSIGHTFACE_HOME", "/models")
-
-RUN_ENV = {**os.environ, "PYTHONUNBUFFERED": "1"}
+INSIGHTFACE_HOME = os.environ.get("INSIGHTFACE_HOME", "/models/.insightface")

 logger = logging.getLogger(__name__)


-def check_models():
-    """Log model status before each run."""
-    print("📦 Checking models...", flush=True)
-    buffalo = Path(INSIGHTFACE_BASE) / ".insightface" / "models" / "buffalo_l"
-    if buffalo.exists():
-        print("  ✅ InsightFace Buffalo_L: present", flush=True)
-    else:
-        print("  ⬇️  InsightFace Buffalo_L: not found — will download", flush=True)
-
+def check_models() -> None:
+    buffalo = Path(INSIGHTFACE_HOME) / "models" / "buffalo_l"
    hf_hub = Path(MODELS_DIR) / "hub"
-    if hf_hub.exists() and any(hf_hub.iterdir()):
-        print("  ✅ HuggingFace models: present", flush=True)
-    else:
-        print("  ⬇️  HuggingFace models: not found — will download", flush=True)
-    print("🚀 Starting winnow...", flush=True)
+    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()
@@ -45,14 +38,15 @@ next_run = cron.get_next(float)
 while True:
    now = time.time()
    if now >= next_run:
-        print(f"\n▶ [{time.strftime('%Y-%m-%d %H:%M:%S')}] Starting winnow...", flush=True)
+        print(f"\n[{time.strftime('%Y-%m-%d %H:%M:%S')}] Starting winnow run...", flush=True)
        check_models()
-        result = subprocess.run(["uv", "run", "winnow"], env=RUN_ENV)
-        if result.returncode != 0:
-            logger.error(f"winnow exited with code {result.returncode}")
-            print(f"❌ winnow failed with exit code {result.returncode}", flush=True)
-        else:
-            print("✅ winnow completed successfully", flush=True)
+        try:
+            main()
+            print("winnow run complete", flush=True)
+        except KeyboardInterrupt:
+            raise
+        except Exception as e:
+            logger.error(f"winnow run failed: {e}", exc_info=True)
+            print(f"winnow run failed: {e}", flush=True)
        next_run = cron.get_next(float)
-    time.sleep(60)
-
+    time.sleep(max(1, next_run - time.time()))
@@ -1,8 +1,6 @@
 """Smoke tests for configuration loading."""

-import os

-import pytest


 def test_config_loads_defaults(monkeypatch):
@@ -27,7 +25,7 @@ def test_config_loads_defaults(monkeypatch):
    assert cfg.FACE_MARGIN == 0.15
    assert cfg.USE_FULL_RESOLUTION is True
    assert cfg.ENABLE_FACE_ALIGNMENT is True
-    assert cfg.ENABLE_CACHE is False
+    assert cfg.ENABLE_CACHE is True

    _Config.reset()

@@ -0,0 +1,151 @@
+"""Tests for image quality filtering functions."""
+
+import numpy as np
+from PIL import Image
+
+
+def _rgb_image(r, g, b, size=(100, 100)) -> Image.Image:
+    arr = np.full((*size, 3), [r, g, b], dtype=np.uint8)
+    return Image.fromarray(arr, "RGB")
+
+
+def _noisy_color_image(size=(100, 100)) -> Image.Image:
+    """Noisy image with a strong red channel so grayscale check passes."""
+    rng = np.random.default_rng(0)
+    arr = rng.integers(0, 256, (*size, 3), dtype=np.uint8)
+    arr[:, :, 0] = np.clip(arr[:, :, 0].astype(int) + 80, 0, 255).astype(np.uint8)
+    arr[:, :, 2] = np.clip(arr[:, :, 2].astype(int) - 80, 0, 255).astype(np.uint8)
+    return Image.fromarray(arr, "RGB")
+
+
+# ── check_blur ────────────────────────────────────────────────────────────────
+
+def test_blur_rejects_flat_image():
+    from winnow.quality import check_blur
+    flat = np.full((100, 100, 3), 128, dtype=np.uint8)
+    passed, reason = check_blur(flat, threshold=100.0)
+    assert not passed
+    assert "Blurry" in reason
+
+
+def test_blur_passes_noisy_color_image():
+    from winnow.quality import check_blur
+    img = _noisy_color_image()
+    passed, _ = check_blur(np.asarray(img), threshold=100.0)
+    assert passed
+
+
+# ── check_grayscale ───────────────────────────────────────────────────────────
+
+def test_grayscale_rejects_ir_image():
+    from winnow.quality import check_grayscale
+    gray = np.full((100, 100, 3), 128, dtype=np.uint8)
+    passed, reason = check_grayscale(gray)
+    assert not passed
+    assert "Grayscale" in reason
+
+
+def test_grayscale_passes_color_image():
+    from winnow.quality import check_grayscale
+    color = np.zeros((100, 100, 3), dtype=np.uint8)
+    color[:, :, 0] = 200  # strong red channel
+    passed, _ = check_grayscale(color)
+    assert passed
+
+
+def test_grayscale_rejects_single_channel():
+    from winnow.quality import check_grayscale
+    single = np.full((100, 100, 1), 128, dtype=np.uint8)
+    passed, reason = check_grayscale(single)
+    assert not passed
+
+
+# ── check_exposure ────────────────────────────────────────────────────────────
+
+def test_exposure_rejects_black_image():
+    from winnow.quality import check_exposure
+    black = np.zeros((100, 100, 3), dtype=np.uint8)
+    passed, reason = check_exposure(black)
+    assert not passed
+    assert "Underexposed" in reason
+
+
+def test_exposure_rejects_white_image():
+    from winnow.quality import check_exposure
+    white = np.full((100, 100, 3), 255, dtype=np.uint8)
+    passed, reason = check_exposure(white)
+    assert not passed
+    assert "Overexposed" in reason
+
+
+def test_exposure_passes_normal_image():
+    from winnow.quality import check_exposure
+    mid = np.full((100, 100, 3), 128, dtype=np.uint8)
+    passed, _ = check_exposure(mid)
+    assert passed
+
+
+# ── check_face_size ───────────────────────────────────────────────────────────
+
+def test_face_size_rejects_small_face():
+    from winnow.quality import check_face_size
+    passed, reason = check_face_size(30, 30, min_px=50)
+    assert not passed
+    assert "small" in reason
+
+
+def test_face_size_passes_adequate_face():
+    from winnow.quality import check_face_size
+    passed, _ = check_face_size(100, 100, min_px=50)
+    assert passed
+
+
+def test_face_size_rejects_if_either_dimension_small():
+    from winnow.quality import check_face_size
+    passed, _ = check_face_size(100, 30, min_px=50)
+    assert not passed
+
+
+# ── check_confidence ──────────────────────────────────────────────────────────
+
+def test_confidence_rejects_low_score():
+    from winnow.quality import check_confidence
+    passed, reason = check_confidence(0.5, min_conf=0.7)
+    assert not passed
+    assert "confidence" in reason.lower()
+
+
+def test_confidence_passes_high_score():
+    from winnow.quality import check_confidence
+    passed, _ = check_confidence(0.95, min_conf=0.7)
+    assert passed
+
+
+def test_confidence_passes_none_score():
+    from winnow.quality import check_confidence
+    passed, _ = check_confidence(None, min_conf=0.7)
+    assert passed
+
+
+# ── assess_quality (integration) ─────────────────────────────────────────────
+
+def test_assess_quality_passes_good_image():
+    from winnow.quality import assess_quality
+    img = _noisy_color_image()
+    result = assess_quality(img, face_bbox=(10, 10, 110, 110), confidence=0.9)
+    assert result.passed
+
+
+def test_assess_quality_collects_multiple_failures():
+    from winnow.quality import assess_quality
+    black = _rgb_image(0, 0, 0)
+    result = assess_quality(black, face_bbox=(0, 0, 10, 10), confidence=0.3)
+    assert not result.passed
+    assert len(result.reasons) >= 2
+
+
+def test_assess_quality_skips_face_size_without_bbox():
+    from winnow.quality import assess_quality
+    img = _noisy_color_image()
+    result = assess_quality(img, face_bbox=None, confidence=0.9)
+    assert result.passed
@@ -1,8 +1,5 @@
 """Tests for upload tracker — mark, filter, reset, and summary logic."""

-import json
-import os
-import tempfile

 import pytest

@@ -1,31 +1,25 @@
 version = 1
 revision = 3
-requires-python = ">=3.12"
+requires-python = ">=3.13"
 resolution-markers = [
-    "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
-    "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
-    "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'darwin'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'darwin'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'darwin'",
-    "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux'",
+    "platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "platform_machine != 's390x' and sys_platform == 'win32'",
+    "platform_machine == 's390x' and sys_platform == 'win32'",
+    "platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
+    "platform_machine != 's390x' and sys_platform == 'darwin'",
+    "platform_machine == 's390x' and sys_platform == 'darwin'",
+    "platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'linux'",
+    "platform_machine == 's390x' and sys_platform == 'linux'",
+]
+required-markers = [
+    "platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "platform_machine == 'aarch64' and sys_platform == 'linux'",
 ]

 [manifest]
-overrides = [{ name = "onnxruntime-gpu", specifier = ">=1.23.2" }]
+overrides = [{ name = "onnxruntime", specifier = ">=1.23.2" }]

 [[package]]
 name = "annotated-doc"
@@ -42,7 +36,6 @@ version = "4.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "idna" },
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" }
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@@ -2238,11 +2140,12 @@ wheels = [

 [[package]]
 name = "ultralytics"
-version = "8.4.64"
+version = "8.4.66"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "matplotlib" },
    { name = "numpy" },
+    { name = "nvidia-ml-py" },
    { name = "opencv-python" },
    { name = "pillow" },
    { name = "polars" },
@@ -2250,16 +2153,19 @@ dependencies = [
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    { name = "requests" },
    { name = "scipy" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 's390x' and sys_platform == 'darwin'" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
    { name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
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+    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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-    { name = "torchvision" },
+    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torchvision", version = "0.27.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.27.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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    { name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
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+    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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@@ -2289,23 +2195,29 @@ wheels = [

 [[package]]
 name = "winnow"
-version = "0.2.0"
+version = "0.2.9"
 source = { editable = "." }
 dependencies = [
    { name = "croniter" },
    { name = "insightface" },
    { name = "numpy" },
-    { name = "nvidia-cudnn-cu12" },
-    { name = "onnxruntime-gpu" },
+    { name = "nvidia-cudnn-cu12", version = "9.10.2.21", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cudnn-cu12", version = "9.23.1.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform != 'linux'" },
+    { name = "onnxruntime" },
+    { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
    { name = "opencv-python-headless" },
    { name = "pillow" },
    { name = "python-dotenv" },
    { name = "requests" },
    { name = "rich" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine != 's390x' and sys_platform == 'darwin'" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
    { name = "torch", version = "2.12.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
-    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torch", version = "2.12.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
    { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torchvision", version = "0.27.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.27.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
    { name = "transformers" },
    { name = "ultralytics" },
 ]
@@ -2322,22 +2234,28 @@ requires-dist = [
    { name = "insightface", specifier = ">=0.7.3" },
    { name = "numpy", specifier = ">=2.2.6" },
    { name = "nvidia-cudnn-cu12", specifier = ">=9.0.0" },
-    { name = "onnxruntime-gpu", specifier = ">=1.23.2" },
+    { name = "onnxruntime", marker = "sys_platform != 'linux'", specifier = ">=1.23.2" },
+    { name = "onnxruntime", marker = "platform_machine != 'x86_64' and sys_platform == 'linux'", specifier = ">=1.23.2" },
+    { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=1.23.2" },
    { name = "opencv-python-headless", specifier = ">=4.12.0.88" },
    { name = "pillow", specifier = ">=12.1.0" },
    { name = "python-dotenv", specifier = ">=1.2.1" },
    { name = "requests", specifier = ">=2.32.5" },
    { name = "rich", specifier = ">=14.2.0" },
-    { name = "torch", marker = "sys_platform != 'linux'", specifier = ">=2.6.0", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torch", marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'", specifier = ">=2.6.0" },
-    { name = "torch", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'", specifier = ">=2.6.0", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=2.6.0", index = "https://download.pytorch.org/whl/cu126" },
+    { name = "torch", marker = "sys_platform != 'linux'", specifier = ">=2.12.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'", specifier = ">=2.12.0" },
+    { name = "torch", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'", specifier = ">=2.12.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=2.12.0", index = "https://download.pytorch.org/whl/cu126" },
+    { name = "torchvision", marker = "sys_platform != 'linux'", specifier = ">=0.27.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torchvision", marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'", specifier = ">=0.27.0" },
+    { name = "torchvision", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'", specifier = ">=0.27.0", index = "https://download.pytorch.org/whl/cpu" },
+    { name = "torchvision", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=0.27.0", index = "https://download.pytorch.org/whl/cu126" },
    { name = "transformers", specifier = ">=4.57.6" },
-    { name = "ultralytics", specifier = ">=8.3.252" },
+    { name = "ultralytics", specifier = ">=8.4.66" },
 ]

 [package.metadata.requires-dev]
 dev = [
    { name = "pytest", specifier = ">=8.0" },
-    { name = "ruff", specifier = ">=0.9.2" },
+    { name = "ruff", specifier = ">=0.15.17" },
 ]
@@ -5,4 +5,9 @@ Immich library for Frigate's Face Recognition (ArcFace) and Object/State
 Classification models.
 """

-__version__ = "0.1.0"
+from importlib.metadata import PackageNotFoundError, version
+
+try:
+    __version__ = version("winnow")
+except PackageNotFoundError:
+    __version__ = "unknown"
@@ -2,6 +2,7 @@

 import logging
 import os
+import sys

 from rich import print as rprint
 from rich.prompt import Confirm
@@ -10,7 +11,7 @@ from .config import Config, ConfigManager
 from .executor import execute_jobs, upload_to_frigate
 from .immich_api import get_people
 from .jobs import _show_preview, auto_configure, interactive_configure
-from .logging import console, setup_logging
+from .log_config import console, setup_logging
 from .upload_tracker import get_person_summary, reset_person

 logger = logging.getLogger(__name__)
@@ -19,7 +20,8 @@ logger = logging.getLogger(__name__)
 def main() -> None:
    """Entry point for winnow CLI."""
    try:
-        setup_logging(verbose=False)
+        verbose = os.environ.get("VERBOSE", "").lower() in ("true", "1", "yes")
+        setup_logging(verbose=verbose)

        console.print(r"""
    [bold blue]winnow[/bold blue]
@@ -48,24 +50,33 @@ def main() -> None:
        if summary:
            rprint("\n[dim]Tracker summary:[/dim]")
            for person_name, counts in summary.items():
-                rprint(f"  [dim]{person_name}: {counts['uploaded']} uploaded, {counts['rejected']} rejected[/dim]")
+                frigate_part = (
+                    f", {counts['frigate_count']} in Frigate"
+                    if counts.get("frigate_count") is not None
+                    else ""
+                )
+                rprint(
+                    f"  [dim]{person_name}: {counts['uploaded']} uploaded,"
+                    f" {counts['rejected']} rejected{frigate_part}[/dim]"
+                )

        people = get_people()
        if not people:
            rprint("[bold red]Could not fetch people from Immich. Check URL/Key.[/bold red]")
            return

-        # Check for non-interactive mode
-        auto_mode = os.environ.get("AUTO_MODE", "false").lower() == "true"
+        # Auto mode when no TTY (Docker, cron, pipes) — the primary use case.
+        # A TTY means local interactive use; AUTO_MODE=true overrides that for scripting.
+        auto_mode = not sys.stdin.isatty() or os.environ.get("AUTO_MODE", "").lower() in ("true", "1", "yes")
        dry_run = os.environ.get("DRY_RUN", "false").lower() in ("true", "1", "yes")

        if dry_run:
            rprint("[bold yellow]DRY RUN — no images will be downloaded or uploaded[/bold yellow]")

        if auto_mode:
-            rprint("[bold cyan]Running in AUTO mode (non-interactive)[/bold cyan]")
            jobs = auto_configure(people)
        else:
+            rprint("[bold cyan]Interactive mode — set AUTO_MODE=true to skip prompts[/bold cyan]")
            jobs = interactive_configure(people)

        if jobs:
@@ -39,8 +39,7 @@ class _Config:
    USE_FULL_RESOLUTION: bool = True
    ENABLE_FACE_ALIGNMENT: bool = True

-    # Caching (opt-in to avoid unexpected files)
-    ENABLE_CACHE: bool = False
+    ENABLE_CACHE: bool = True
    CACHE_DIR: str = ".if_cache"

    def __new__(cls) -> "_Config":
@@ -64,15 +63,14 @@ class _Config:
        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")
        self.ENABLE_FACE_ALIGNMENT = os.getenv("ENABLE_FACE_ALIGNMENT", "true").lower() in ("true", "1", "yes")
-        self.ENABLE_CACHE = os.getenv("ENABLE_CACHE", "false").lower() in ("true", "1", "yes")
+        self.ENABLE_CACHE = os.getenv("ENABLE_CACHE", "true").lower() in ("true", "1", "yes")
        self.CACHE_DIR = os.getenv("CACHE_DIR", ".if_cache")

-        # Fall back to config file for missing values
+        # Fall back to config file for non-sensitive values (API_KEY not stored here)
        if CONFIG_FILE.exists():
            try:
                data = json.loads(CONFIG_FILE.read_text())
                self.IMMICH_URL = self.IMMICH_URL or data.get("IMMICH_URL")
-                self.API_KEY = self.API_KEY or data.get("API_KEY")
                if not os.getenv("OUTPUT_DIR"):
                    self.OUTPUT_DIR = data.get("OUTPUT_DIR", self.OUTPUT_DIR)
            except (json.JSONDecodeError, OSError) as e:
@@ -84,13 +82,16 @@ class _Config:
        cls._instance = None

    def save(self) -> None:
-        """Persist configuration to file."""
+        """Persist non-sensitive configuration to file.
+
+        API_KEY is intentionally excluded — store it in .env or as an
+        environment variable instead of a plain-text config file.
+        """
        try:
            CONFIG_FILE.write_text(
                json.dumps(
                    {
                        "IMMICH_URL": self.IMMICH_URL,
-                        "API_KEY": self.API_KEY,
                        "OUTPUT_DIR": self.OUTPUT_DIR,
                    },
                    indent=2,
@@ -113,8 +114,8 @@ class _Config:

        if not self.API_KEY:
            console.print("[yellow]Immich API Key not found.[/yellow]")
+            console.print("[dim]Tip: set API_KEY in your .env file to avoid re-entering it.[/dim]")
            self.API_KEY = Prompt.ask("Enter Immich API Key", password=True)
-            self.save()

    def validate(self) -> None:
        """Raise ValueError if required config is missing."""
@@ -159,7 +160,12 @@ class _ConfigAccessor:


 Config = _ConfigAccessor()
-ConfigManager = type("ConfigManager", (), {"get": staticmethod(lambda: _Config())})
+
+
+class ConfigManager:
+    @staticmethod
+    def get() -> _Config:
+        return _Config()


 def get_headers() -> dict[str, str]:
@@ -29,6 +29,7 @@ def select_diverse_assets(
    entity_name: str,
    selection_mode: str = "smart",
    entity_type: str = "face",
+    person_id: str | None = None,
    progress_callback=None,
 ) -> list:
    """
@@ -59,7 +60,7 @@ def select_diverse_assets(
        return _select_time_spread(assets, limit)

    try:
-        return _select_by_embedding(assets, limit, entity_type, progress_callback)
+        return _select_by_embedding(assets, limit, entity_type, 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)
@@ -80,9 +81,11 @@ def _fetch_thumbnail(asset_id: str, timeout: int = 10) -> Image.Image | None:
        return None


-def _get_face_bbox(asset: dict) -> tuple[float, float, float, float] | None:
-    """Extract face bounding box from asset metadata if available."""
+def _get_face_bbox(asset: dict, person_id: str | None = None) -> tuple[float, float, float, float] | None:
+    """Extract face bounding box from asset metadata for the given person."""
    for person in asset.get("people", []):
+        if person_id and person.get("id") != person_id:
+            continue
        faces = person.get("faces", [])
        if faces:
            f = faces[0]
@@ -95,12 +98,16 @@ def _get_face_bbox(asset: dict) -> tuple[float, float, float, float] | None:
    return None


-def _get_face_confidence(asset: dict) -> float | None:
-    """Extract face detection confidence from asset metadata if available."""
+def _get_face_confidence(asset: dict, person_id: str | None = None) -> float | None:
+    """Extract face detection confidence from asset metadata for the given person."""
    for person in asset.get("people", []):
+        if person_id and person.get("id") != person_id:
+            continue
        faces = person.get("faces", [])
        if faces:
-            return faces[0].get("score") or faces[0].get("confidence")
+            f = faces[0]
+            score = f.get("score")
+            return score if score is not None else f.get("confidence")
    return None


@@ -108,6 +115,7 @@ def _crop_face_from_thumbnail(
    img: Image.Image,
    asset: dict,
    margin: float = 0.25,
+    person_id: str | None = None,
 ) -> Image.Image | None:
    """Crop the face region from a thumbnail using Immich bbox metadata.

@@ -118,19 +126,22 @@ def _crop_face_from_thumbnail(
        img: Full preview thumbnail
        asset: Asset dict with people/faces metadata
        margin: Extra margin around the bbox (fraction, default 25%)
+        person_id: If provided, only crop from this person's face data.

    Returns:
        Cropped face PIL image, or None if no face metadata available
    """
-    bbox = _get_face_bbox(asset)
+    bbox = _get_face_bbox(asset, person_id=person_id)
    if bbox is None:
        return None

    x1, y1, x2, y2 = bbox
    img_w, img_h = img.size

-    # Get metadata dimensions to scale bbox
+    # Get metadata dimensions to scale bbox — must match the same person as _get_face_bbox
    for person in asset.get("people", []):
+        if person_id and person.get("id") != person_id:
+            continue
        faces = person.get("faces", [])
        if faces:
            meta_w = faces[0].get("imageWidth") or img_w
@@ -169,6 +180,7 @@ def _select_by_embedding(
    assets: list,
    limit: int | str,
    entity_type: str,
+    person_id: str | None = None,
    progress_callback=None,
 ) -> list:
    """Select assets using embedding-based cluster-aware FPS.
@@ -217,11 +229,11 @@ def _select_by_embedding(
        if img is None:
            continue

-        confidence = _get_face_confidence(asset)
+        confidence = _get_face_confidence(asset, person_id=person_id)

        # Quality gate: filter before expensive embedding computation
        if entity_type == "face":
-            face_bbox = _get_face_bbox(asset)
+            face_bbox = _get_face_bbox(asset, person_id=person_id)
            quality = assess_quality(
                img,
                face_bbox=face_bbox,
@@ -236,7 +248,7 @@ def _select_by_embedding(
                continue

            # Crop the target person's face before embedding
-            face_crop = _crop_face_from_thumbnail(img, asset)
+            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
@@ -360,7 +372,7 @@ def _compute_adaptive_threshold(emb_normed: np.ndarray, entity_type: str) -> flo
    fraction = 0.20 if entity_type == "face" else 0.10
    threshold = max(0.05, median_dist * fraction)

-    logger.info(
+    logger.debug(
        f"Adaptive threshold: {threshold:.4f} "
        f"(median_dist={median_dist:.4f}, fraction={fraction}, type={entity_type})"
    )
@@ -402,7 +414,7 @@ def _cluster_aware_selection(

    # --- Stage 1: K-Medoids clustering ---
    k = min(max(5, target // 4), n // 3, n)  # e.g., 5-20 clusters
-    logger.info(f"Clustering {n} embeddings into {k} groups (K-Medoids)...")
+    logger.debug(f"Clustering {n} embeddings into {k} groups (K-Medoids)...")

    # Compute full cosine distance matrix
    dist_matrix = 1 - emb_normed @ emb_normed.T
@@ -411,7 +423,7 @@ def _cluster_aware_selection(
    selected = list(medoid_indices)
    selected_set = set(selected)

-    logger.info(f"Selected {len(selected)} cluster medoids as initial picks.")
+    logger.debug(f"Selected {len(selected)} cluster medoids as initial picks.")

    # --- Stage 2: FPS with hard example weighting ---
    min_dists = np.full(n, np.inf)
@@ -436,8 +448,8 @@ def _cluster_aware_selection(
            break  # All points selected

        if limit == "auto" and best_dist < auto_threshold:
-            logger.info(
-                f"Auto-stop: Next best image {best_dist:.3f} away " f"(adaptive threshold {auto_threshold:.4f})."
+            logger.debug(
+                f"Auto-stop: next best image {best_dist:.3f} away (adaptive threshold {auto_threshold:.4f})."
            )
            break

@@ -6,11 +6,13 @@ Unified embedding interface for faces and objects.
 - Caching: Disk-based cache avoids recomputation on reruns
 """

-import contextlib
 import importlib
 import logging
 import os
+import time
 import warnings
+from contextlib import contextmanager
+from pathlib import Path

 import cv2
 import numpy as np
@@ -20,6 +22,26 @@ from .cache import get_cache

 logger = logging.getLogger(__name__)

+
+@contextmanager
+def _suppress_output():
+    """Suppress stdout/stderr at the file-descriptor level, silencing C extension noise."""
+    devnull_fd = os.open(os.devnull, os.O_WRONLY)
+    saved_out, saved_err = os.dup(1), os.dup(2)
+    try:
+        os.dup2(devnull_fd, 1)
+        os.dup2(devnull_fd, 2)
+        yield
+    finally:
+        try:
+            os.dup2(saved_out, 1)
+        finally:
+            os.dup2(saved_err, 2)
+        os.close(devnull_fd)
+        os.close(saved_out)
+        os.close(saved_err)
+
+
 # Lazy-loaded singletons
 _insightface_app = None
 _insightface_loaded = False
@@ -37,18 +59,14 @@ def _preload_cuda_libs() -> None:
    """Preload CUDA/cuDNN DLLs so onnxruntime-gpu registers CUDAExecutionProvider.

    Starting with onnxruntime-gpu 1.19+, CUDA/cuDNN libraries are no longer
-    bundled inside the ORT package. They must be loaded from the nvidia-*
-    pip packages (nvidia-cuda-runtime-cu12, nvidia-cudnn-cu12) before any
-    InferenceSession is created.
-
-    Calling preload_dlls() with directory="" searches NVIDIA site-packages
-    directories automatically.
+    bundled inside the ORT package — they come from the nvidia-* pip packages.
+    preload_dlls() locates them automatically via site-packages discovery.
    """
    try:
        import onnxruntime
        if hasattr(onnxruntime, "preload_dlls"):
-            onnxruntime.preload_dlls(cuda=True, cudnn=True, directory="")
-            logger.info("Preloaded CUDA/cuDNN DLLs for onnxruntime-gpu")
+            onnxruntime.preload_dlls(cuda=True, cudnn=True)
+            logger.debug("Preloaded CUDA/cuDNN DLLs for onnxruntime-gpu")
        else:
            logger.debug("onnxruntime.preload_dlls() not available (ORT < 1.21)")
    except Exception as e:
@@ -70,32 +88,48 @@ def get_insightface_app():
    # Preload CUDA/cuDNN DLLs BEFORE any ORT InferenceSession is created
    _preload_cuda_libs()

+    ctx_id = -1
+    insightface_home = os.environ.get("INSIGHTFACE_HOME", os.path.expanduser("~/.insightface"))
    try:
        import onnxruntime as ort
        from insightface.app import FaceAnalysis

+        # Disk cache check — lets the user know whether a download is coming
+        buffalo_path = Path(insightface_home) / "models" / "buffalo_l"
+        if buffalo_path.exists() and any(buffalo_path.iterdir()):
+            logger.info("InsightFace Buffalo_L: found in model cache")
+        else:
+            logger.info("InsightFace Buffalo_L: not cached — downloading now (~300 MB)")
+
        # Get providers, excluding TensorRT to avoid noisy errors
        providers = [p for p in ort.get_available_providers() if p != "TensorrtExecutionProvider"]
-        logger.info(f"Available ONNX providers: {providers}")
+        logger.debug(f"ONNX providers available: {providers}")

-        # Determine device: 0 for GPU, -1 for CPU
        gpu_providers = {
            "CUDAExecutionProvider",
            "ROCmExecutionProvider",
            "MPSExecutionProvider",
            "CoreMLExecutionProvider",
        }
-        ctx_id = -1 if _is_force_cpu() else (0 if gpu_providers & set(providers) else -1)
+        has_gpu_provider = bool(gpu_providers & set(providers))
+        ctx_id = -1 if _is_force_cpu() else (0 if has_gpu_provider else -1)
+
+        if not has_gpu_provider and not _is_force_cpu():
+            logger.warning(
+                "No GPU execution provider found — running InsightFace on CPU. "
+                "If you have an NVIDIA GPU, ensure the NVIDIA Container Toolkit is "
+                "installed and the container has GPU access (deploy.resources in compose)."
+            )

        device_str = "GPU" if ctx_id >= 0 else "CPU"
-        logger.info(f"Loading InsightFace Buffalo_L on {device_str} (ctx_id={ctx_id})...")
+        logger.info(f"InsightFace Buffalo_L: loading into memory on {device_str}...")

-        # Suppress C-level output during model loading
-        with open(os.devnull, "w") as devnull, contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):
-            insightface_home = os.environ.get("INSIGHTFACE_HOME", os.path.expanduser("~/.insightface"))
+        t0 = time.time()
+        with _suppress_output():
            _insightface_app = FaceAnalysis(name="buffalo_l", root=insightface_home, providers=providers)
            _insightface_app.prepare(ctx_id=ctx_id, det_size=(640, 640))

+        logger.info(f"InsightFace Buffalo_L: ready on {device_str} ({time.time() - t0:.1f}s)")
        return _insightface_app

    except ImportError:
@@ -103,17 +137,23 @@ def get_insightface_app():
        return None
    except Exception as e:
        logger.error(f"Failed to load InsightFace: {e}")
-        # Retry on CPU if GPU failed
        if ctx_id == 0:
-            logger.warning("Retrying InsightFace on CPU...")
+            logger.warning("InsightFace GPU load failed — retrying on CPU...")
            try:
                from insightface.app import FaceAnalysis

-                _insightface_app = FaceAnalysis(name="buffalo_l", root=insightface_home)
-                _insightface_app.prepare(ctx_id=-1, det_size=(640, 640))
+                t0 = time.time()
+                with _suppress_output():
+                    _insightface_app = FaceAnalysis(
+                        name="buffalo_l",
+                        root=insightface_home,
+                        providers=["CPUExecutionProvider"],
+                    )
+                    _insightface_app.prepare(ctx_id=-1, det_size=(640, 640))
+                logger.info(f"InsightFace Buffalo_L: ready on CPU (fallback, {time.time() - t0:.1f}s)")
                return _insightface_app
            except Exception as ex:
-                logger.error(f"CPU fallback failed: {ex}")
+                logger.error(f"InsightFace CPU fallback failed: {ex}")
        return None


@@ -162,7 +202,18 @@ def get_siglip_model():
        from transformers import AutoImageProcessor, SiglipVisionModel

        model_name = "google/siglip-base-patch16-224"
-        logger.info(f"Loading SigLIP model ({model_name})...")
+
+        # 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)
@@ -176,15 +227,16 @@ def get_siglip_model():
        if not _is_force_cpu():
            if torch.cuda.is_available():
                _siglip_model = _siglip_model.cuda()
-                logger.info("SigLIP running on CUDA GPU")
+                device_name = "CUDA GPU"
            elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
                _siglip_model = _siglip_model.to("mps")
-                logger.info("SigLIP running on Apple MPS")
+                device_name = "Apple MPS"
            else:
-                logger.info("SigLIP running on CPU")
+                device_name = "CPU"
        else:
-            logger.info("FORCE_CPU set. SigLIP running on CPU")
+            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:
@@ -267,30 +319,31 @@ def get_embedding(

    use_cache = Config.ENABLE_CACHE and asset_id is not None
    cache = get_cache(Config.CACHE_DIR) if use_cache else None
-    model_key = "immich" if entity_type == "face" else "siglip"
+    # 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, model_key)
+            cache.put(asset_id, immich_embedding, cache_key)
        return immich_embedding

    # 2. Check disk cache
    if cache:
-        cached = cache.get(asset_id, model_key)
+        cached = cache.get(asset_id, cache_key)
        if cached is not None:
            return cached

    # 3. Compute locally
    if entity_type == "face":
        emb = get_face_embedding(img_pil)
-        model_key = "insightface"
    else:
        emb = get_object_embedding(img_pil)

-    # Cache the result
    if emb is not None and cache:
-        cache.put(asset_id, emb, model_key)
+        cache.put(asset_id, emb, cache_key)

    return emb

@@ -14,7 +14,7 @@ from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn
 from .config import Config, get_headers
 from .image_processing import process_face_mode, process_full_mode, process_object_mode
 from .immich_api import fetch_face_data, fetch_full_image
-from .logging import console
+from .log_config import console
 from .upload_tracker import mark_rejected, mark_uploaded

 logger = logging.getLogger(__name__)
@@ -58,6 +58,7 @@ def _enrich_asset_with_face_data(asset: dict, person: dict) -> dict:

    # Inject into asset so process_face_mode can find it via asset["people"]
    asset["people"] = [{"id": person_id, "faces": [face_info]}]
+    asset["face_confidence"] = face_data.confidence
    return asset


@@ -96,8 +97,9 @@ def execute_jobs(jobs: list[dict]) -> None:
                shutil.rmtree(person_dir)
            os.makedirs(person_dir, exist_ok=True)

-            # Track filename → asset_id mapping for upload dedup
+            # Track filename → asset_id and filename → confidence score
            asset_map: dict[str, str] = {}
+            score_map: dict[str, float | None] = {}

            count = 0
            for asset in assets:
@@ -132,11 +134,13 @@ def execute_jobs(jobs: list[dict]) -> None:
                            # Record which asset produced which output file
                            filename = f"{count}.jpg"
                            asset_map[filename] = asset["id"]
+                            score_map[filename] = asset.get("face_confidence")
                            # 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:
@@ -149,8 +153,9 @@ def execute_jobs(jobs: list[dict]) -> None:
                progress.advance(job_task)
                progress.advance(overall_task)

-            # Store asset_map on the job so upload_to_frigate can use it
+            # Store maps on the job so upload_to_frigate can use them
            job["asset_map"] = asset_map
+            job["score_map"] = score_map

            progress.remove_task(job_task)

@@ -230,6 +235,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                continue

            asset_map = filename_to_asset_id.get(name, {})
+            score_map = job.get("score_map", {})
            person_files = sorted(asset_map.keys())

            if not person_files:
@@ -257,7 +263,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                            # Mark this asset as uploaded so it's skipped on future runs
                            asset_id = asset_map.get(fname)
                            if asset_id:
-                                mark_uploaded(asset_id, person_name=name)
+                                mark_uploaded(asset_id, person_name=name, score=score_map.get(fname))

                            break
                        else:
@@ -0,0 +1,33 @@
+"""Frigate API helpers for querying face training state."""
+
+import logging
+import os
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+
+def get_frigate_face_counts() -> dict[str, int] | None:
+    """Return {person_name: training_image_count} from Frigate's train directory.
+
+    Returns None if FRIGATE_URL is not set or the API is unreachable, so callers
+    can distinguish "API unavailable" from "person has 0 images."
+    """
+    frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/")
+    if not frigate_url:
+        return None
+    try:
+        resp = requests.get(f"{frigate_url}/api/faces", timeout=10)
+        resp.raise_for_status()
+        data = resp.json()
+        # Response: {person_name: [file, ...], "train": [...], ...}
+        # "train" is a flat pending list, not a person — skip it.
+        return {
+            name: len(files)
+            for name, files in data.items()
+            if name != "train" and isinstance(files, list)
+        }
+    except Exception as e:
+        logger.warning(f"Could not query Frigate face counts: {e}")
+        return None
@@ -35,10 +35,13 @@ def get_people() -> list[dict]:
            headers=get_headers(),
            timeout=10,
        )
+        if resp.status_code == 401:
+            logger.error("Immich API key is invalid or expired (401 Unauthorized). Update API_KEY.")
+            return []
        resp.raise_for_status()
        return resp.json().get("people", [])
    except (requests.RequestException, ValueError) as e:
-        logger.error(f"Failed to fetch people: {e}")
+        logger.error(f"Failed to fetch people from Immich: {e}")
        return []


@@ -49,7 +52,7 @@ def fetch_all_assets(person: dict) -> list[dict]:
    url = f"{Config.IMMICH_URL}/api/search/metadata"
    page_size = 1000

-    logger.info(f"Fetching assets for {name}...")
+    logger.debug(f"Fetching assets for {name}...")

    assets = []
    for page in range(1, MAX_PAGES + 1):
@@ -137,10 +140,11 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
            face.get("boundingBoxY2", 0),
        )

+        score = face.get("score")
        return FaceData(
            embedding=embedding,
            bbox=bbox,
-            confidence=face.get("score") or face.get("confidence"),
+            confidence=score if score is not None else face.get("confidence"),
            image_width=face.get("imageWidth", 0),
            image_height=face.get("imageHeight", 0),
        )
@@ -212,6 +216,6 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d
        except ValueError:
            continue

-    logger.info(f"Retained {len(recent)} assets (filtered {skipped} old assets).")
+    logger.debug(f"Retained {len(recent)} assets (filtered {skipped} old assets).")
    return recent

@@ -11,9 +11,10 @@ from rich.table import Table
 from .config import Config
 from .diversity import select_diverse_assets
 from .embeddings import is_embedding_available, load_embedding_model
+from .frigate_api import get_frigate_face_counts
 from .immich_api import fetch_all_assets, filter_recent_assets
-from .logging import console
-from .upload_tracker import filter_already_uploaded
+from .log_config import console
+from .upload_tracker import filter_already_uploaded, get_person_summary, update_frigate_count

 logger = logging.getLogger(__name__)

@@ -80,7 +81,9 @@ def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, st
    return strategy_map.get(strategy, ("auto", "smart"))


-def _perform_selection(assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str) -> list:
+def _perform_selection(
+    assets: list, limit: int | str, name: str, selection_mode: str, entity_type: 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)"
@@ -103,6 +106,7 @@ def _perform_selection(assets: list, limit: int | str, name: str, selection_mode
                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),
            )

@@ -112,7 +116,9 @@ def _perform_selection(assets: list, limit: int | str, name: str, selection_mode

    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)
+        selected = select_diverse_assets(
+            assets, limit, name, selection_mode="time", entity_type=entity_type, person_id=person_id
+        )
    rprint(f"  [green]Selected {len(selected)} images using time spread.[/green]")
    return selected

@@ -144,8 +150,11 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:

    rprint(f"  Found [bold]{len(all_assets)}[/bold] total, [bold]{len(recent_assets)}[/bold] in range ({years} years).")

-    # Filter out assets already uploaded to Frigate
-    retry_rejected = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes")
+    # Filter out assets already uploaded to Frigate.
+    # In interactive mode, ask — use the env var only as the default so it can
+    # still be pre-set (e.g. RETRY_REJECTED=true) without forcing the answer.
+    retry_env = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes")
+    retry_rejected = Confirm.ask("Include previously rejected images?", default=retry_env)
    before_dedup = len(recent_assets)
    new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected))
    recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids]
@@ -166,7 +175,9 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
        return None

    # Perform selection
-    selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, entity_type)
+    selected_assets = _perform_selection(
+        recent_assets, limit, name, selection_mode, entity_type, 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}
@@ -236,6 +247,12 @@ def auto_configure(people: list[dict]) -> list[dict]:
                f" ≥{min_face_count} assets (MIN_FACE_COUNT={min_face_count})"
            )

+    frigate_counts = get_frigate_face_counts()
+    # Persist each count to tracker so the last known value survives Frigate downtime
+    if frigate_counts is not None:
+        for pname, count in frigate_counts.items():
+            update_frigate_count(pname, count)
+    upload_summary = get_person_summary()
    jobs = []
    for person in valid_people:
        name = person["name"]
@@ -263,13 +280,43 @@ def auto_configure(people: list[dict]) -> list[dict]:
            rprint(f"  [dim]Skipping {name} (0 new images after dedup).[/dim]")
            continue

+        # Enforce MAX_AUTO_IMAGES as a lifetime cap per person.
+        # Priority: live Frigate count → last cached Frigate count → local uploaded count.
+        person_summary = upload_summary.get(name, {})
+        if frigate_counts is not None:
+            already_uploaded = frigate_counts.get(name, 0)
+        else:
+            fc = person_summary.get("frigate_count")
+            already_uploaded = fc if fc is not None else person_summary.get("uploaded", 0)
+        capacity = Config.MAX_AUTO_IMAGES - already_uploaded
+        if capacity <= 0:
+            rprint(
+                f"  [dim]Skipping {name} (at lifetime cap:"
+                f" {already_uploaded}/{Config.MAX_AUTO_IMAGES} trained).[/dim]"
+            )
+            continue
+
        has_embedding = is_embedding_available(entity_type)
        limit, selection_mode = _resolve_strategy(strategy, has_embedding)

+        # Cap selection to remaining capacity.
+        # For auto mode with partial training, keep "auto" so adaptive stopping
+        # still runs — just trim the result to the remaining capacity afterward.
+        auto_cap = None
+        if limit == "auto":
+            if already_uploaded > 0:
+                auto_cap = capacity
+        else:
+            limit = min(limit, capacity)
+
        if selection_mode == "skip":
            continue

-        selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, entity_type)
+        selected_assets = _perform_selection(
+            recent_assets, limit, name, selection_mode, entity_type, person_id=person["id"]
+        )
+        if auto_cap is not None:
+            selected_assets = selected_assets[:auto_cap]

        if selected_assets:
            rprint(f"  [green]Queued {len(selected_assets)} images for {name}.[/green]")
@@ -26,10 +26,12 @@ def setup_logging(verbose: bool = False) -> logging.Logger:
    """Configure logging with Rich console and file output."""
    level = logging.DEBUG if verbose else logging.INFO

-    # Configure root logger
+    # Configure root logger; close existing handlers before replacing them
    root = logging.getLogger()
    root.setLevel(level)
-    root.handlers.clear()
+    for h in root.handlers[:]:
+        h.close()
+        root.removeHandler(h)

    # Rich console handler - uses shared console to avoid breaking progress bars
    root.addHandler(RichHandler(rich_tracebacks=True, markup=True, console=console))
@@ -37,7 +39,7 @@ def setup_logging(verbose: bool = False) -> logging.Logger:
    # File handler (always debug level) — log file respects OUTPUT_DIR if set
    log_dir = os.environ.get("OUTPUT_DIR", ".")
    os.makedirs(log_dir, exist_ok=True)
-    log_path = os.path.join(log_dir, "immich_export.log")
+    log_path = os.path.join(log_dir, "winnow.log")
    file_handler = logging.FileHandler(log_path)
    file_handler.setLevel(logging.DEBUG)
    file_handler.setFormatter(logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s"))
@@ -8,6 +8,13 @@ Both are excluded from future candidate pools. To reset:
  - All:           delete both files
  - One person:    call reset_person("Name") or set RESET_PERSON=Name
  - Rejects only:  delete frigate_rejected_ids.json, or set RETRY_REJECTED=true
+
+by_person schema (frigate_uploaded_ids.json):
+  {
+    "asset_ids":    ["immich-id-1", ...],   # all assets we attempted to upload
+    "scores":       {"immich-id-1": 0.953}, # Immich face confidence at upload time
+    "frigate_count": 42                     # last known Frigate training image count
+  }
 """

 import json
@@ -55,7 +62,23 @@ def _load_flat(filename: str) -> set[str]:
    return set(_load(filename).get(_flat_key(filename), []))


-def _mark(filename: str, asset_id: str, person_name: str | None) -> None:
+def _get_ids(entry: list | dict) -> list[str]:
+    """Extract asset_ids from either the old list format or the new dict format."""
+    if isinstance(entry, list):
+        return entry
+    return entry.get("asset_ids", [])
+
+
+def _migrate_entry(entry: list | dict) -> dict:
+    """Ensure by_person entry is in the current dict format."""
+    if isinstance(entry, list):
+        return {"asset_ids": sorted(entry), "scores": {}}
+    entry.setdefault("asset_ids", [])
+    entry.setdefault("scores", {})
+    return entry
+
+
+def _mark(filename: str, asset_id: str, person_name: str | None, score: float | None = None) -> None:
    data = _load(filename)
    flat_key = _flat_key(filename)
    flat = set(data.get(flat_key, []))
@@ -63,9 +86,13 @@ def _mark(filename: str, asset_id: str, person_name: str | None) -> None:
    data[flat_key] = sorted(flat)
    if person_name:
        by_person = data.setdefault("by_person", {})
-        person_ids = set(by_person.get(person_name, []))
-        person_ids.add(asset_id)
-        by_person[person_name] = sorted(person_ids)
+        entry = _migrate_entry(by_person.get(person_name, {}))
+        ids = set(entry["asset_ids"])
+        ids.add(asset_id)
+        entry["asset_ids"] = sorted(ids)
+        if score is not None:
+            entry["scores"][asset_id] = round(score, 4)
+        by_person[person_name] = entry
    _save(filename, data)


@@ -79,8 +106,8 @@ def load_rejected_ids() -> set[str]:
    return _load_flat(REJECT_TRACKER_FILE)


-def mark_uploaded(asset_id: str, person_name: str | None = None) -> None:
-    _mark(UPLOAD_TRACKER_FILE, asset_id, person_name)
+def mark_uploaded(asset_id: str, person_name: str | None = None, score: float | None = None) -> None:
+    _mark(UPLOAD_TRACKER_FILE, asset_id, person_name, score=score)
    logger.debug(f"Marked {asset_id} as uploaded ({person_name})")


@@ -89,14 +116,25 @@ def mark_rejected(asset_id: str, person_name: str | None = None) -> None:
    logger.debug(f"Marked {asset_id} as rejected ({person_name})")


+def update_frigate_count(person_name: str, count: int) -> None:
+    """Record Frigate's authoritative training image count for a person."""
+    data = _load(UPLOAD_TRACKER_FILE)
+    by_person = data.setdefault("by_person", {})
+    entry = _migrate_entry(by_person.get(person_name, {}))
+    entry["frigate_count"] = count
+    by_person[person_name] = entry
+    _save(UPLOAD_TRACKER_FILE, data)
+
+
 def reset_person(person_name: str) -> None:
    """Remove all uploaded and rejected records for a given person."""
    for filename in (UPLOAD_TRACKER_FILE, REJECT_TRACKER_FILE):
        data = _load(filename)
        flat_key = _flat_key(filename)
        by_person = data.get("by_person", {})
-        person_ids = set(by_person.pop(person_name, []))
-        if person_ids:
+        entry = by_person.pop(person_name, None)
+        if entry is not None:
+            person_ids = set(_get_ids(entry))
            flat = set(data.get(flat_key, [])) - person_ids
            data[flat_key] = sorted(flat)
            data["by_person"] = by_person
@@ -104,18 +142,22 @@ def reset_person(person_name: str) -> None:
    logger.info(f"Reset tracking data for {person_name}")


-def get_person_summary() -> dict[str, dict[str, int]]:
-    """Return {person_name: {uploaded: N, rejected: N}} for display."""
-    uploaded_by = _load(UPLOAD_TRACKER_FILE).get("by_person", {})
-    rejected_by = _load(REJECT_TRACKER_FILE).get("by_person", {})
-    names = set(uploaded_by) | set(rejected_by)
-    return {
-        name: {
-            "uploaded": len(uploaded_by.get(name, [])),
-            "rejected": len(rejected_by.get(name, [])),
+def get_person_summary() -> dict[str, dict]:
+    """Return {person_name: {uploaded, rejected, frigate_count, scores}} for display/capacity."""
+    uploaded_data = _load(UPLOAD_TRACKER_FILE).get("by_person", {})
+    rejected_data = _load(REJECT_TRACKER_FILE).get("by_person", {})
+    names = set(uploaded_data) | set(rejected_data)
+    result = {}
+    for name in sorted(names):
+        u_entry = uploaded_data.get(name, {})
+        r_entry = rejected_data.get(name, {})
+        result[name] = {
+            "uploaded": len(_get_ids(u_entry)),
+            "rejected": len(_get_ids(r_entry)),
+            "frigate_count": u_entry.get("frigate_count") if isinstance(u_entry, dict) else None,
+            "scores": u_entry.get("scores", {}) if isinstance(u_entry, dict) else {},
        }
-        for name in sorted(names)
-    }
+    return result


 def filter_already_uploaded(