release: merge dev → main for 0.4.0

Frigate pre-upload scoring, quality replacement inversion, bootstrap fix,
FRIGATE_SCORE_CEILING / ENABLE_FRIGATE_SCORES, removal of post-upload gate.
See CHANGELOG.md [0.4.0] for the full list.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.env.example

@@ -23,13 +23,16 @@ STRATEGY=auto
 # YEARS_FILTER=10             # Only include images from the last N years (default: 10)
 
 # ── Image Quality ─────────────────────────────────────────────────────────────
-# MIN_FACE_WIDTH=50           # Minimum face width in pixels (default: 50)
+# MIN_FACE_WIDTH=90           # Minimum face width in pixels (default: 90, guarantees ≥8,100px crop)
 # FACE_MARGIN=0.15            # Padding around face crop as fraction (default: 0.15)
 # ENABLE_FACE_ALIGNMENT=true  # Align face before cropping (default: true)
 # USE_FULL_RESOLUTION=true    # Use full-res images vs thumbnails (default: true)
 # MIN_CONFIDENCE=0.7          # Minimum face detection confidence (default: 0.7)
-# BLUR_THRESHOLD=100.0        # Laplacian blur threshold; lower = accept more blur (default: 100.0)
+# BLUR_THRESHOLD=120.0        # Laplacian blur threshold; lower = accept more blur (default: 120.0)
 # MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 80)
+# QUALITY_REPLACEMENT=true    # At cap, replace a weaker tracked image with a better candidate (default: true)
+# FRIGATE_SCORE_CEILING=0.0   # Skip uploads already well-covered (pre-upload score > ceiling = redundant; 0 = disabled; requires at least one prior run)
+# ENABLE_FRIGATE_SCORES=true  # Call Frigate's recognize endpoint pre-upload to store diversity scores (default: true; adds ~200ms per upload)
 
 # ── Caching & Models ──────────────────────────────────────────────────────────
 # FORCE_CPU=true              # Disable GPU, fall back to CPU

.github/workflows/docker-publish.yml

@@ -53,7 +53,7 @@ jobs:
 
       - name: Set up QEMU
         if: matrix.platform == 'linux/arm64'
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v4
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v4
@@ -67,7 +67,7 @@ jobs:
 
       - name: Build and push by digest
         id: build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
@@ -164,7 +164,7 @@ jobs:
         uses: actions/checkout@v6
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v4
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v4
@@ -186,7 +186,7 @@ jobs:
           fi
 
       - name: Build and push CPU image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
@@ -248,7 +248,7 @@ jobs:
           fi
 
       - name: Build and push ROCm image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
@@ -310,7 +310,7 @@ jobs:
           fi
 
       - name: Build and push Intel image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile

.github/workflows/release.yml

@@ -15,11 +15,13 @@ concurrency:
 
 jobs:
   release:
-    name: Create GitHub Release & Build Image
+    name: Create GitHub Release
     runs-on: ubuntu-latest
     permissions:
       contents: write
-      packages: write
+    outputs:
+      tag: ${{ steps.tag.outputs.TAG }}
+      version: ${{ steps.tag.outputs.VERSION }}
     steps:
       - name: Free up disk space
         run: |
@@ -60,12 +62,6 @@ jobs:
           uv lock
           rm _pyproject_orig.toml
 
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v4
-
       - name: Resolve tag name
         id: tag
         run: |
@@ -131,6 +127,30 @@ jobs:
               prerelease: false,
             });
 
+  build-gpu:
+    name: Build GPU image
+    needs: release
+    runs-on: ubuntu-latest
+    permissions:
+      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
+        uses: actions/checkout@v6
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v4
+
       - name: Log in to GHCR
         uses: docker/login-action@v4
         with:
@@ -139,21 +159,52 @@ jobs:
           password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build and push GPU image (latest)
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
           platforms: linux/amd64,linux/arm64
           push: true
-          build-args: VERSION=${{ steps.tag.outputs.VERSION }}
+          build-args: VERSION=${{ needs.release.outputs.version }}
           cache-from: type=gha,scope=release-gpu
           cache-to: type=gha,mode=max,scope=release-gpu
           tags: |
             ghcr.io/sudolulo/winnow:latest
-            ghcr.io/sudolulo/winnow:${{ steps.tag.outputs.TAG }}
+            ghcr.io/sudolulo/winnow:${{ needs.release.outputs.tag }}
+
+  build-cpu:
+    name: Build CPU image
+    needs: release
+    runs-on: ubuntu-latest
+    permissions:
+      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
+        uses: actions/checkout@v6
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v4
+
+      - name: Log in to GHCR
+        uses: docker/login-action@v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build and push CPU image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
@@ -161,15 +212,46 @@ jobs:
           push: true
           build-args: |
             VARIANT=cpu
-            VERSION=${{ steps.tag.outputs.VERSION }}
+            VERSION=${{ needs.release.outputs.version }}
           cache-from: type=gha,scope=release-cpu
           cache-to: type=gha,mode=max,scope=release-cpu
           tags: |
             ghcr.io/sudolulo/winnow:cpu
-            ghcr.io/sudolulo/winnow:${{ steps.tag.outputs.TAG }}-cpu
+            ghcr.io/sudolulo/winnow:${{ needs.release.outputs.tag }}-cpu
+
+  build-rocm:
+    name: Build ROCm image
+    needs: release
+    runs-on: ubuntu-latest
+    permissions:
+      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
+        uses: actions/checkout@v6
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v4
+
+      - name: Log in to GHCR
+        uses: docker/login-action@v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build and push ROCm image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
@@ -177,15 +259,46 @@ jobs:
           push: true
           build-args: |
             VARIANT=rocm
-            VERSION=${{ steps.tag.outputs.VERSION }}
+            VERSION=${{ needs.release.outputs.version }}
           cache-from: type=gha,scope=release-rocm
           cache-to: type=gha,mode=max,scope=release-rocm
           tags: |
             ghcr.io/sudolulo/winnow:rocm
-            ghcr.io/sudolulo/winnow:${{ steps.tag.outputs.TAG }}-rocm
+            ghcr.io/sudolulo/winnow:${{ needs.release.outputs.tag }}-rocm
+
+  build-intel:
+    name: Build Intel image
+    needs: release
+    runs-on: ubuntu-latest
+    permissions:
+      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
+        uses: actions/checkout@v6
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v4
+
+      - name: Log in to GHCR
+        uses: docker/login-action@v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
 
       - name: Build and push Intel image
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: ./Dockerfile
@@ -193,9 +306,9 @@ jobs:
           push: true
           build-args: |
             VARIANT=intel
-            VERSION=${{ steps.tag.outputs.VERSION }}
+            VERSION=${{ needs.release.outputs.version }}
           cache-from: type=gha,scope=release-intel
           cache-to: type=gha,mode=max,scope=release-intel
           tags: |
             ghcr.io/sudolulo/winnow:intel
-            ghcr.io/sudolulo/winnow:${{ steps.tag.outputs.TAG }}-intel
+            ghcr.io/sudolulo/winnow:${{ needs.release.outputs.tag }}-intel

.github/workflows/update-lockfile.yml

@@ -3,6 +3,8 @@ name: Update lockfiles
 
 on:
   push:
+    branches:
+      - '**'
     paths:
       - 'pyproject.toml'
       - 'pyproject-cpu.toml'

CHANGELOG.md

@@ -7,6 +7,57 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.4.0] - 2026-06-13
+
+### Added
+
+- **Pre-upload Frigate recognition scores**: `recognize_face` is now called before each upload to measure how novel the candidate is relative to the existing training set. The score is stored in the tracker (`frigate_scores` field) and drives quality replacement in subsequent runs. Adds ~200 ms per upload.
+- **`ENABLE_FRIGATE_SCORES`** (default `true`): controls all pre-upload Frigate recognize calls. Set `false` to use blur-score replacement only and skip the Frigate round-trip entirely.
+- **`FRIGATE_SCORE_CEILING`** (default `0.0`): skip uploads whose pre-upload recognize score already exceeds this value — those face conditions are already well-covered by the training set. `0` disables (no ceiling); requires at least one prior run to have stored scores.
+- **`get_most_redundant_mapped_file()`**: new upload-tracker function that returns the mapped file with the highest Frigate pre-upload score. High score = the training set already covers that face condition well = the best deletion target for quality replacement.
+- **Cold-start notice**: first run (no existing Frigate model) now logs a clear message explaining why Frigate scores are unavailable and that they will populate on subsequent runs.
+- **4 new tests** for `get_most_redundant_mapped_file` covering score ordering, ties, excludes, and no-score cases.
+
+### Changed
+
+- **Quality replacement now uses Frigate scores**: when Frigate scores are available, at-cap replacement targets the _most redundant_ mapped file (highest pre-upload score) and replaces it only when the candidate is _more novel_ (lower score). Falls back to blur-score comparison when no Frigate scores have been stored yet.
+- **`recognize_face` returns `(face_name, score) | None`** instead of `float | None`: the caller now validates that the recognized person matches the expected person before using the score. Wrong-person scores no longer drive ceiling skips or replacement decisions.
+- **Bootstrap fix**: recognize was previously called below-cap only when `FRIGATE_SCORE_CEILING > 0`, so `frigate_scores` was never populated with default settings and the Frigate replacement path never activated. Recognize is now called for all below-cap uploads when `ENABLE_FRIGATE_SCORES=true`, seeding scores for future at-cap runs regardless of ceiling setting.
+- **Batch GET `/api/faces`**: Frigate file-count lookups are now batched to reduce round-trip overhead on runs with many people.
+- **Skip candidate download on low Frigate confidence**: candidates where the Immich detection confidence is below threshold are now filtered before the full-resolution download, saving bandwidth.
+
+### Removed
+
+- **Post-upload quality gate (`FRIGATE_SCORE_THRESHOLD`)**: enforcement of a Frigate score threshold after upload has been removed. Post-upload scores are taken after the image is already in the training set, so the model has already retrained on it — deleting it at that point is wasteful and disrupts the model for the next Frigate run. Pre-upload scoring (`FRIGATE_SCORE_CEILING`) provides a cleaner signal at the right moment.
+
+### Fixed
+
+- **Frigate replacement path never activated with default settings**: with `FRIGATE_SCORE_CEILING=0.0` (default), the bootstrap call to `recognize_face` was gated behind `CEILING > 0`, so `frigate_scores` stayed empty, `has_frigate_scores` was always False, and the Frigate replacement branch was permanently unreachable. Removing the ceiling guard from the below-cap recognize call breaks the circular dependency.
+- **Schema comment contradiction**: `upload_tracker.py` line-16 comment described `frigate_scores` as "post-upload" while the block comment on lines 22–24 said "pre-upload". Corrected to "pre-upload" throughout.
+- **README default values**: `MIN_FACE_WIDTH` was documented as `50` (actual default: `90`); `BLUR_THRESHOLD` was documented as `100.0` (actual default: `120.0`). Both corrected.
+- **README missing env vars**: `FRIGATE_SCORE_CEILING` and `ENABLE_FRIGATE_SCORES` were present in `config.py` and `.env.example` but absent from the README env var table. Both added.
+- **README quality-replacement description**: Step 8 and the `QUALITY_REPLACEMENT` row now document the dual-mode behaviour (Frigate-score path and blur-score fallback) instead of describing only the original blur-score path.
+
+## [0.3.3] - 2026-06-13
+
+### Fixed
+
+- **`MIN_FACE_WIDTH` default raised from 50 → 90px**: 50px crops produce 2,500–4,225 total pixels, well below Frigate's own camera capture range of 16k–50k px. 90px guarantees ≥8,100 total pixels even when face margins are fully clipped by image edges, keeping winnow training crops above the floor Frigate considers useful.
+
+## [0.3.2] - 2026-06-13
+
+### Added
+
+- **Crop dimension tracing**: winnow now records the pixel dimensions (width × height) of each face crop at upload time in the tracker (`crop_dims` field). Run `TRACE_CROP_SIZE=3848 winnow` to look up which Immich asset produced a crop with that pixel dimension — output includes person name, asset ID, Immich URL, blur score, and the Frigate filename. Useful for tracing low-quality or unexpected images visible in Frigate back to their source.
+
+## [0.3.1] - 2026-06-13
+
+### Fixed
+
+- **Lint**: split overly long line in `quality.py` (`E501`, 146 → ≤120 chars).
+- **CI — lockfile update workflow**: added `branches: ['**']` filter to `on.push` so tag pushes no longer trigger the job; tag checkouts land in detached HEAD and the subsequent `git push` had no branch target.
+- **CI — release workflow**: split four Docker image builds into parallel jobs (`build-gpu`, `build-cpu`, `build-rocm`, `build-intel`), each with its own runner. Previously all four ran in a single job; building the GPU and CPU multi-platform images exhausted disk, causing ROCm and Intel builds to be cancelled.
+
 ## [0.3.0] - 2026-06-13
 
 ### Added

README.md

@@ -2,6 +2,9 @@
 
 [![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) [![GitHub release](https://img.shields.io/github/v/release/sudolulo/winnow)](https://github.com/sudolulo/winnow/releases/latest) [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](LICENSE) [![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)
 
+> **Early Development — Use With Caution**
+> winnow is functional but still maturing. Features that modify your Frigate training data — quality replacement, stale mapping cleanup — can remove images from your dataset and are not yet battle-tested at scale. Review the logs after each run and keep backups of your Frigate face training directory until you are confident in the results.
+
 **Docs:** [Setup](https://github.com/sudolulo/winnow/wiki/Setup) · [Troubleshooting](https://github.com/sudolulo/winnow/wiki/Troubleshooting) · [FAQ](https://github.com/sudolulo/winnow/wiki/FAQ)
 
 `winnow` pulls photos from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition and object classification models.
@@ -56,9 +59,11 @@ Immich library
 8. Deliver
    • Face mode: upload crops to Frigate's face registration API
      ↳ below MAX_AUTO_IMAGES — upload freely
-     ↳ at cap + QUALITY_REPLACEMENT=true — swap the lowest-scoring tracked
-       image if the new candidate scores higher; manually added files are
-       never touched
+     ↳ at cap + QUALITY_REPLACEMENT=true — with Frigate scoring active,
+       swap the most redundant tracked image (highest pre-upload recognize
+       score) if the candidate is more novel (lower score); falling back to
+       blur-score comparison when no Frigate scores are available; manually
+       added files are never touched
      ↳ at cap + QUALITY_REPLACEMENT=false — skip this person
    • Object mode: save crops to disk → place into your Frigate data directory
 ```
@@ -183,14 +188,16 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `MIN_FACE_WIDTH` | `50` | Minimum face crop width in pixels |
+| `MIN_FACE_WIDTH` | `90` | Minimum face crop width in pixels |
 | `FACE_MARGIN` | `0.15` | Padding around bounding box crop (fraction of face size) |
 | `ENABLE_FACE_ALIGNMENT` | `true` | Align to ArcFace 112×112 format using facial landmarks |
 | `USE_FULL_RESOLUTION` | `true` | Download full-resolution originals rather than preview thumbnails |
 | `MIN_CONFIDENCE` | `0.7` | Minimum Immich face detection confidence |
-| `BLUR_THRESHOLD` | `100.0` | Laplacian variance threshold — lower accepts more blur |
+| `BLUR_THRESHOLD` | `120.0` | Laplacian variance threshold — lower accepts more blur |
 | `MAX_AUTO_IMAGES` | `80` | Maximum training images per person in Frigate |
-| `QUALITY_REPLACEMENT` | `true` | When at cap, swap the lowest-scoring tracked image for a better candidate. Never touches manually added Frigate files. Set `false` to skip people already at cap |
+| `QUALITY_REPLACEMENT` | `true` | When at cap, swap a weaker tracked image for a better candidate. With Frigate scoring active, targets the most redundant image (highest pre-upload recognize score); otherwise uses blur score. Never touches manually added Frigate files. Set `false` to skip people at cap |
+| `FRIGATE_SCORE_CEILING` | `0.0` | Skip uploads whose pre-upload Frigate recognize score exceeds this value — they are already well-covered. `0` disables; requires at least one prior run to have scores |
+| `ENABLE_FRIGATE_SCORES` | `true` | Call Frigate's recognize endpoint pre-upload to store diversity scores used for quality replacement. Adds ~200 ms per upload. Disable to use blur-score replacement only |
 
 ### GPU & Models
 

pyproject-cpu.toml

@@ -25,7 +25,7 @@ dependencies = [
     "rich>=14.2.0",
     "torch>=2.12.0",
     "torchvision>=0.27.0",
-    "transformers>=4.57.6",
+    "transformers>=5.12.0",
     "ultralytics>=8.4.66",
 ]
 

pyproject-intel.toml

@@ -25,7 +25,7 @@ dependencies = [
     "rich>=14.2.0",
     "torch>=2.12.0",
     "torchvision>=0.27.0",
-    "transformers>=4.57.6",
+    "transformers>=5.12.0",
     "ultralytics>=8.4.66",
 ]
 

pyproject-rocm.toml

@@ -25,7 +25,7 @@ dependencies = [
     "rich>=14.2.0",
     "torch>=2.5.0",
     "torchvision>=0.20.0",
-    "transformers>=4.57.6",
+    "transformers>=5.12.0",
     "ultralytics>=8.4.66",
 ]
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.3.0"
+version = "0.4.0"
 description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition and object classification."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
@@ -29,7 +29,7 @@ dependencies = [
     "rich>=14.2.0",
     "torch>=2.12.0",
     "torchvision>=0.27.0",
-    "transformers>=4.57.6",
+    "transformers>=5.12.0",
     "ultralytics>=8.4.66",
 ]
 

tests/test_config.py

@@ -17,9 +17,9 @@ def test_config_loads_defaults(monkeypatch):
     assert cfg.API_KEY == "test-key"
     assert cfg.OUTPUT_DIR == "./frigate_train"
     assert cfg.YEARS_FILTER == 10
-    assert cfg.MIN_FACE_WIDTH == 50
+    assert cfg.MIN_FACE_WIDTH == 90
     assert cfg.MIN_FACE_COUNT == 0
-    assert cfg.BLUR_THRESHOLD == 100.0
+    assert cfg.BLUR_THRESHOLD == 120.0
     assert cfg.MIN_CONFIDENCE == 0.7
     assert cfg.MAX_AUTO_IMAGES == 80
     assert cfg.QUALITY_REPLACEMENT is True

tests/test_upload_tracker.py

@@ -218,3 +218,45 @@ def test_get_lowest_quality_exclude_all_returns_none():
     mark_uploaded("asset-a", person_name="Alice", score=0.50)
     record_frigate_file("Alice", "Alice-a.webp", "asset-a")
     assert get_lowest_quality_mapped_file("Alice", exclude={"Alice-a.webp"}) is None
+
+
+# ── get_most_redundant_mapped_file ────────────────────────────────────────────
+
+def test_get_most_redundant_none_when_no_frigate_scores():
+    from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file
+    mark_uploaded("asset-a", person_name="Alice", score=0.80)
+    record_frigate_file("Alice", "Alice-a.webp", "asset-a")
+    # blur score only, no frigate_score → no candidates
+    assert get_most_redundant_mapped_file("Alice") is None
+
+
+def test_get_most_redundant_returns_highest_frigate_score():
+    from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file
+    mark_uploaded("asset-novel", person_name="Alice", score=0.50, frigate_score=0.31)
+    mark_uploaded("asset-redundant", person_name="Alice", score=0.90, frigate_score=0.88)
+    record_frigate_file("Alice", "Alice-novel.webp", "asset-novel")
+    record_frigate_file("Alice", "Alice-redundant.webp", "asset-redundant")
+    result = get_most_redundant_mapped_file("Alice")
+    assert result is not None
+    frigate_filename, asset_id, score = result
+    assert frigate_filename == "Alice-redundant.webp"
+    assert asset_id == "asset-redundant"
+    assert score == pytest.approx(0.88, abs=0.001)
+
+
+def test_get_most_redundant_exclude_skips_file():
+    from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file
+    mark_uploaded("asset-hi", person_name="Alice", score=0.9, frigate_score=0.85)
+    mark_uploaded("asset-lo", person_name="Alice", score=0.5, frigate_score=0.40)
+    record_frigate_file("Alice", "Alice-hi.webp", "asset-hi")
+    record_frigate_file("Alice", "Alice-lo.webp", "asset-lo")
+    result = get_most_redundant_mapped_file("Alice", exclude={"Alice-hi.webp"})
+    assert result is not None
+    assert result[1] == "asset-lo"  # hi excluded; lo is next highest
+
+
+def test_get_most_redundant_exclude_all_returns_none():
+    from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file
+    mark_uploaded("asset-a", person_name="Alice", score=0.5, frigate_score=0.70)
+    record_frigate_file("Alice", "Alice-a.webp", "asset-a")
+    assert get_most_redundant_mapped_file("Alice", exclude={"Alice-a.webp"}) is None

uv-cpu.lock

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-version = "5.11.0"
+version = "5.12.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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     { name = "typer" },
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+    { url = "https://files.pythonhosted.org/packages/ff/1f/d385913c38e900d23b728a4188fee625f2fccb306aeebc59be9d91404a5a/transformers-5.12.0-py3-none-any.whl", hash = "sha256:500be9eb644ede81c3103eee7687fc36d05dd75d1c76686c3820b26396fe7c7c", size = 11150246, upload-time = "2026-06-12T14:39:17.009Z" },
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 [[package]]
@@ -1832,7 +1832,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.2.9"
+version = "0.2.13"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },
@@ -1873,7 +1873,7 @@ requires-dist = [
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     { name = "torchvision", marker = "platform_machine != 'x86_64' or sys_platform != 'linux'", specifier = ">=0.27.0" },
     { name = "torchvision", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=0.27.0", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "transformers", specifier = ">=4.57.6" },
+    { name = "transformers", specifier = ">=5.12.0" },
     { name = "ultralytics", specifier = ">=8.4.66" },
 ]
 

uv-intel.lock

@@ -1889,7 +1889,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.2.11"
+version = "0.2.13"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },
@@ -1930,7 +1930,7 @@ requires-dist = [
     { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=2.12.0", index = "https://download.pytorch.org/whl/cpu" },
     { name = "torchvision", marker = "platform_machine != 'x86_64' or sys_platform != 'linux'", specifier = ">=0.27.0" },
     { name = "torchvision", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=0.27.0", index = "https://download.pytorch.org/whl/cpu" },
-    { name = "transformers", specifier = ">=4.57.6" },
+    { name = "transformers", specifier = ">=5.12.0" },
     { name = "ultralytics", specifier = ">=8.4.66" },
 ]
 

uv-rocm.lock

@@ -409,7 +409,7 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "1.18.0"
+version = "1.19.0"
 source = { registry = "https://pypi.org/simple" }
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-version = "3.10.9"
+version = "3.11.0"
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 dependencies = [
     { name = "croniter" },
@@ -1922,7 +1922,7 @@ requires-dist = [
     { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=2.5.0", index = "https://download.pytorch.org/whl/rocm6.3" },
     { name = "torchvision", marker = "platform_machine != 'x86_64' or sys_platform != 'linux'", specifier = ">=0.20.0" },
     { name = "torchvision", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=0.20.0", index = "https://download.pytorch.org/whl/rocm6.3" },
-    { name = "transformers", specifier = ">=4.57.6" },
+    { name = "transformers", specifier = ">=5.12.0" },
     { name = "ultralytics", specifier = ">=8.4.66" },
 ]
 

uv.lock

@@ -1970,15 +1970,16 @@ version = "2.12.0"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "platform_machine != 's390x' and sys_platform == 'darwin'",
+    "platform_machine == 's390x' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "filelock", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "fsspec", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "jinja2", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "networkx", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "setuptools", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "sympy", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "typing-extensions", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "filelock", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "fsspec", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "jinja2", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "networkx", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "setuptools", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "sympy", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "typing-extensions", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
 wheels = [
     { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.12.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:90dd587a5f61bfe1307148b581e2084fc5bc4a06e2b90a20e9a36b81087ff16b", upload-time = "2026-05-12T16:20:17Z" },
@@ -2041,16 +2042,15 @@ resolution-markers = [
     "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'",
 ]
 dependencies = [
-    { name = "filelock", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "fsspec", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "jinja2", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "networkx", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "setuptools", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "sympy", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "typing-extensions", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "filelock", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "fsspec", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "jinja2", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "networkx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "setuptools", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "sympy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "typing-extensions", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
 wheels = [
     { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.12.0%2Bcpu-cp313-cp313-linux_s390x.whl", hash = "sha256:5e0da19e1c3bfdc9b92638c552579eac678354485d61fc8921b0461fd6c40449", upload-time = "2026-05-12T23:17:05Z" },
@@ -2116,11 +2116,12 @@ version = "0.27.0"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "platform_machine != 's390x' and sys_platform == 'darwin'",
+    "platform_machine == 's390x' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "numpy", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "pillow", marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "numpy", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "pillow", marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
 wheels = [
     { url = "https://download-r2.pytorch.org/whl/cpu/torchvision-0.27.0-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:41d6dae73e1af09fa82ded597ae57f2a2314285acde54b25890a8f8e51b999d7", upload-time = "2026-05-12T16:20:37Z" },
@@ -2171,12 +2172,11 @@ resolution-markers = [
     "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'",
 ]
 dependencies = [
-    { name = "numpy", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "pillow", marker = "(platform_machine == 's390x' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "numpy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "pillow", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
 wheels = [
     { url = "https://download-r2.pytorch.org/whl/cpu/torchvision-0.27.0%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:69093b64b2762c43df17be2db2be163029963d90bc3f1801500fdeb723e54833", upload-time = "2026-05-12T16:20:36Z" },
@@ -2306,13 +2306,13 @@ dependencies = [
     { name = "pyyaml" },
     { 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') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { 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') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { 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') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "torchvision", version = "0.27.0+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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "ultralytics-thop" },
 ]
@@ -2327,9 +2327,9 @@ version = "2.0.20"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
-    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { 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') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/98/c6/d25cb53e141242f74950744179c21b8798bb09b9e7161465ecda4f577ddf/ultralytics_thop-2.0.20.tar.gz", hash = "sha256:f3595e0d8c6fd0b9f62fc2cd9be921755e2649a05c34f1fabaea0bff7295d641", size = 34682, upload-time = "2026-06-06T11:42:42.184Z" }
@@ -2348,7 +2348,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.2.13"
+version = "0.3.3"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },
@@ -2363,13 +2363,13 @@ dependencies = [
     { 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') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "torch", version = "2.12.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { 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') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 's390x' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { name = "torchvision", version = "0.27.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { 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') or (platform_machine == 'aarch64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (platform_machine == 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
-    { name = "torchvision", version = "0.27.0+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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
+    { 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') or (sys_platform == 'darwin' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform == 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" },
     { name = "transformers" },
     { name = "ultralytics" },
@@ -2403,7 +2403,7 @@ requires-dist = [
     { 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 = "transformers", specifier = ">=5.12.0" },
     { name = "ultralytics", specifier = ">=8.4.66" },
 ]
 

winnow/cli.py

@@ -12,17 +12,55 @@ from .executor import execute_jobs, upload_to_frigate
 from .immich_api import get_people
 from .jobs import _show_preview, auto_configure, interactive_configure
 from .log_config import console, setup_logging
-from .upload_tracker import get_person_summary, reset_person
+from .upload_tracker import find_by_crop_dimension, get_person_summary, reset_person
 
 logger = logging.getLogger(__name__)
 
 
+def _handle_trace_crop(size_str: str) -> None:
+    """Print tracker records whose crop dimension matches the given pixel size and exit."""
+    try:
+        size = int(size_str)
+    except ValueError:
+        rprint(f"[bold red]TRACE_CROP_SIZE must be an integer, got: {size_str!r}[/bold red]")
+        sys.exit(1)
+
+    immich_url = os.environ.get("IMMICH_URL", "").rstrip("/")
+    matches = find_by_crop_dimension(size)
+    if not matches:
+        rprint(f"[yellow]No crops with dimension {size}px found in tracker.[/yellow]")
+        rprint("[dim]Note: crop dimensions are only recorded for uploads made after this feature was added.[/dim]")
+        sys.exit(0)
+
+    rprint(f"\n[bold]Crops matching dimension {size}px:[/bold] ({len(matches)} found)\n")
+    for m in matches:
+        rprint(f"  [bold cyan]{m['person']}[/bold cyan]")
+        rprint(f"    Dimensions:   {m['width']}×{m['height']}px")
+        rprint(f"    Asset ID:     {m['asset_id']}")
+        if immich_url:
+            rprint(f"    Immich URL:   {immich_url}/photos/{m['asset_id']}")
+        blur = m.get("blur_score")
+        rprint(f"    Blur score:   {blur:.1f}" if blur is not None else "    Blur score:   unknown")
+        fscore = m.get("frigate_score")
+        rprint(f"    Frigate score: {fscore:.2f}" if fscore is not None else "    Frigate score: unknown")
+        if m.get("frigate_filename"):
+            rprint(f"    Frigate file: {m['frigate_filename']}")
+        else:
+            rprint("    Frigate file: [dim]unmapped (reconciliation race)[/dim]")
+        rprint()
+    sys.exit(0)
+
+
 def main() -> None:
     """Entry point for winnow CLI."""
     try:
         verbose = os.environ.get("VERBOSE", "").lower() in ("true", "1", "yes")
         setup_logging(verbose=verbose)
 
+        trace_size = os.environ.get("TRACE_CROP_SIZE", "").strip()
+        if trace_size:
+            _handle_trace_crop(trace_size)
+
         console.print(r"""
     [bold blue]winnow[/bold blue]
     [dim]Immich -> Frigate Training Data Curator[/dim]

winnow/config.py

@@ -26,11 +26,13 @@ class _Config:
     YEARS_FILTER: int = 10
 
     # Quality filtering
-    MIN_FACE_WIDTH: int = 50
-    BLUR_THRESHOLD: float = 100.0
+    MIN_FACE_WIDTH: int = 90
+    BLUR_THRESHOLD: float = 120.0
     MIN_CONFIDENCE: float = 0.7
     MAX_AUTO_IMAGES: int = 80
     QUALITY_REPLACEMENT: bool = True
+    FRIGATE_SCORE_CEILING: float = 0.0
+    ENABLE_FRIGATE_SCORES: bool = True
 
     # People filtering
     MIN_FACE_COUNT: int = 0
@@ -56,12 +58,14 @@ class _Config:
         self.API_KEY = os.getenv("API_KEY")
         self.OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./frigate_train")
         self.YEARS_FILTER = int(os.getenv("YEARS_FILTER", "10"))
-        self.MIN_FACE_WIDTH = int(os.getenv("MIN_FACE_WIDTH", "50"))
+        self.MIN_FACE_WIDTH = int(os.getenv("MIN_FACE_WIDTH", "90"))
         self.MIN_FACE_COUNT = int(os.getenv("MIN_FACE_COUNT", "0"))
-        self.BLUR_THRESHOLD = float(os.getenv("BLUR_THRESHOLD", "100.0"))
+        self.BLUR_THRESHOLD = float(os.getenv("BLUR_THRESHOLD", "120.0"))
         self.MIN_CONFIDENCE = float(os.getenv("MIN_CONFIDENCE", "0.7"))
         self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "80"))
         self.QUALITY_REPLACEMENT = os.getenv("QUALITY_REPLACEMENT", "true").lower() in ("true", "1", "yes")
+        self.FRIGATE_SCORE_CEILING = float(os.getenv("FRIGATE_SCORE_CEILING", "0.0"))
+        self.ENABLE_FRIGATE_SCORES = os.getenv("ENABLE_FRIGATE_SCORES", "true").lower() in ("true", "1", "yes")
         self.FACE_MARGIN = float(os.getenv("FACE_MARGIN", "0.15"))
         self.USE_FULL_RESOLUTION = os.getenv("USE_FULL_RESOLUTION", "true").lower() in ("true", "1", "yes")
         self.ENABLE_FACE_ALIGNMENT = os.getenv("ENABLE_FACE_ALIGNMENT", "true").lower() in ("true", "1", "yes")

winnow/executor.py

@@ -13,15 +13,17 @@ from rich import print as rprint
 from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
 
 from .config import Config, get_headers
-from .frigate_api import delete_frigate_person_files, get_frigate_person_files
+from .frigate_api import delete_frigate_person_files, get_all_frigate_person_files, get_frigate_person_files, recognize_face
 from .image_processing import process_face_mode, process_full_mode, process_object_mode
 from .immich_api import fetch_face_data, fetch_full_image
 from .log_config import console
 from .quality import assess_quality
 from .upload_tracker import (
     get_lowest_quality_mapped_file,
+    get_most_redundant_mapped_file,
     get_tracked_frigate_file_count,
     get_tracked_frigate_filenames,
+    has_frigate_scores,
     mark_rejected,
     mark_uploaded,
     record_frigate_file,
@@ -170,9 +172,10 @@ def execute_jobs(jobs: list[dict]) -> None:
                 shutil.rmtree(person_dir)
             os.makedirs(person_dir, exist_ok=True)
 
-            # Track filename → asset_id and filename → confidence score
+            # Track filename → asset_id, filename → confidence score, filename → crop dims
             asset_map: dict[str, str] = {}
             score_map: dict[str, float | None] = {}
+            dims_map: dict[str, tuple[int, int]] = {}
 
             count = 0
             for asset in assets:
@@ -181,6 +184,17 @@ def execute_jobs(jobs: list[dict]) -> None:
                     # from the Immich faces API (not included in search/metadata results)
                     if mode == "face":
                         asset = _enrich_asset_with_face_data(asset, person)
+                        # Skip download if detection confidence already disqualifies
+                        # the asset — avoids fetching a large image we'll discard.
+                        conf = asset.get("face_confidence")
+                        if conf is not None and conf < Config.MIN_CONFIDENCE:
+                            progress.console.print(
+                                f"[yellow]Skipped {asset['id']}"
+                                f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
+                            )
+                            progress.advance(job_task)
+                            progress.advance(overall_task)
+                            continue
 
                     # Use full-resolution for final output when configured
                     if use_full_res:
@@ -208,6 +222,8 @@ def execute_jobs(jobs: list[dict]) -> None:
                             filename = f"{count}.jpg"
                             asset_map[filename] = asset["id"]
                             score_map[filename] = asset.get("quality_score")
+                            if mode == "face" and isinstance(saved, tuple):
+                                dims_map[filename] = saved
                             # Time-spread path: compute blur score from the downloaded
                             # image. Cap at 1440px so the scale matches the preview
                             # thumbnails the embedding path uses for scoring — Laplacian
@@ -244,6 +260,7 @@ def execute_jobs(jobs: list[dict]) -> None:
             # Store maps on the job so upload_to_frigate can use them
             job["asset_map"] = asset_map
             job["score_map"] = score_map
+            job["dims_map"] = dims_map
 
             progress.remove_task(job_task)
 
@@ -301,6 +318,10 @@ def upload_to_frigate(jobs: list[dict]) -> None:
     uploaded, failed = 0, 0
     max_retries = 2
 
+    # Fetch all Frigate training files once — avoids one GET /api/faces per person.
+    # Falls back to per-person calls inside the loop if this fetch fails.
+    all_frigate_files = get_all_frigate_person_files()
+
     with Progress(
         SpinnerColumn(),
         TextColumn("[progress.description]{task.description}"),
@@ -324,6 +345,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
 
             asset_map = filename_to_asset_id.get(name, {})
             score_map = job.get("score_map", {})
+            dims_map = job.get("dims_map", {})
             person_files = sorted(asset_map.keys())
 
             if not person_files:
@@ -337,7 +359,10 @@ def upload_to_frigate(jobs: list[dict]) -> None:
             # Snapshot live Frigate files for post-upload reconciliation diff only.
             # effective_count is sourced from the tracker (mapped files) so that
             # manually-added Frigate files don't consume winnow's managed quota.
-            _snapshot = get_frigate_person_files(name)
+            _snapshot = (
+                all_frigate_files.get(name, []) if all_frigate_files is not None
+                else get_frigate_person_files(name)
+            )
             if _snapshot is None:
                 # Frigate GET is down; fall back to the tracker's mapped filenames
                 # as the pre-upload baseline. reconciliation will still work unless
@@ -349,8 +374,24 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                 known_frigate_files_at_start: set[str] = get_tracked_frigate_filenames(name)
             else:
                 known_frigate_files_at_start: set[str] = set(_snapshot)
+                # Remove tracker mappings for files that no longer exist in Frigate
+                # (manually deleted, or cleaned up outside winnow). This corrects the
+                # effective_count so those slots are available for new uploads.
+                stale = get_tracked_frigate_filenames(name) - known_frigate_files_at_start
+                for stale_fn in stale:
+                    remove_frigate_file(name, stale_fn)
+                if stale:
+                    progress.console.print(
+                        f"  [dim]{name}: cleared {len(stale)} stale mapping(s)"
+                        " (file(s) no longer in Frigate)[/dim]"
+                    )
             effective_count = get_tracked_frigate_file_count(name)
+            pre_run_count = effective_count
             quality_replacement = job.get("config", {}).get("quality_replacement", False)
+            if Config.ENABLE_FRIGATE_SCORES and pre_run_count == 0:
+                progress.console.print(
+                    f"  [dim]{name}: first run — Frigate diversity scoring will apply from the next run[/dim]"
+                )
             actually_uploaded: list[tuple[str, str | None]] = []
             failed_deletes: set[str] = set()
             min_quality_score_for_slot: float | None = None
@@ -373,40 +414,106 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         continue
 
                 at_cap = effective_count >= Config.MAX_AUTO_IMAGES
+
+                # Pre-upload Frigate score — clean measurement (image not yet in training set).
+                # Called for all below-cap uploads (seeds frigate_scores for future at-cap
+                # replacement) and for at-cap uploads when scores already exist. Skipped on
+                # the first run (pre_run_count == 0) since Frigate has no model yet.
+                # recognize_face returns (face_name, score); we only use the score when the
+                # best match is for the correct person. Mismatches (or "unknown") are treated
+                # as None so a wrong-person score never drives a ceiling skip or replacement.
+                # Frigate rebuilds its model asynchronously after any delete (clear + background
+                # thread), so the first recognize call after a deletion returns None — our code
+                # handles this conservatively by skipping that candidate until the next run.
+                pre_fscore: float | None = None
+                if Config.ENABLE_FRIGATE_SCORES and pre_run_count > 0:
+                    if not at_cap or has_frigate_scores(name):
+                        _result = recognize_face(fpath)
+                        if _result is not None and _result[0] == name:
+                            pre_fscore = _result[1]
+
+                # Ceiling check: skip if the existing training set already covers this
+                # face condition well. Applies below cap only — at cap, replacement logic
+                # drives the decision.
+                if not at_cap and Config.FRIGATE_SCORE_CEILING > 0 and pre_run_count > 0:
+                    if pre_fscore is not None and pre_fscore > Config.FRIGATE_SCORE_CEILING:
+                        progress.console.print(
+                            f"    [dim]⏭  {fname}: Frigate score {pre_fscore:.2f}"
+                            f" > ceiling {Config.FRIGATE_SCORE_CEILING:.2f}, already covered[/dim]"
+                        )
+                        progress.advance(upload_task)
+                        continue
+
                 if at_cap:
                     if not quality_replacement:
                         progress.console.print(f"    [dim]⏭  {fname}: at cap, quality replacement disabled[/dim]")
                         progress.advance(upload_task)
                         continue
-                    new_score = score_map.get(fname)
-                    if new_score is None:
-                        progress.console.print(f"    [dim]⏭  {fname}: no confidence score, skipping replacement[/dim]")
-                        progress.advance(upload_task)
-                        continue
-                    worst = get_lowest_quality_mapped_file(name, exclude=failed_deletes)
-                    if worst is None or new_score <= worst[2]:
-                        worst_score_str = f"{worst[2]:.3f}" if worst is not None else "N/A"
+
+                    using_fscore = has_frigate_scores(name) and Config.ENABLE_FRIGATE_SCORES
+                    if using_fscore:
+                        candidate_score = pre_fscore
+                        if candidate_score is None:
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: Frigate recognize unavailable, skipping replacement[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        # Low score = more novel than the most redundant mapped file = replace
+                        target = get_most_redundant_mapped_file(name, exclude=failed_deletes)
+                        if target is None or candidate_score >= target[2]:
+                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: frigate {candidate_score:.3f} ≥ most redundant"
+                                f" {target_score_str}, not more novel[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        target_frigate_file, _target_asset_id, target_score = target
                         progress.console.print(
-                            f"    [dim]⏭  {fname}: score {new_score:.3f} ≤ worst mapped"
-                            f" {worst_score_str}, skipping[/dim]"
+                            f"    🔄 {fname}: frigate {candidate_score:.3f} < {target_score:.3f},"
+                            f" replacing {target_frigate_file} (more novel)"
                         )
-                        progress.advance(upload_task)
-                        continue
-                    # Delete the worst mapped file to make room for the better one
-                    worst_frigate_file, _worst_asset_id, worst_score = worst
-                    progress.console.print(
-                        f"    🔄 {fname}: score {new_score:.3f} > {worst_score:.3f},"
-                        f" replacing {worst_frigate_file}"
-                    )
-                    if delete_frigate_person_files(name, [worst_frigate_file]):
-                        remove_frigate_file(name, worst_frigate_file)
-                        effective_count -= 1
-                        min_quality_score_for_slot = worst_score
+                        if delete_frigate_person_files(name, [target_frigate_file]):
+                            remove_frigate_file(name, target_frigate_file)
+                            effective_count -= 1
+                            min_quality_score_for_slot = None  # clear any blur-mode slot floor — Frigate uses a different score metric
+                        else:
+                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
+                            failed_deletes.add(target_frigate_file)
+                            progress.advance(upload_task)
+                            continue
                     else:
-                        logger.warning(f"Failed to delete {worst_frigate_file} for {name}, skipping replacement")
-                        failed_deletes.add(worst_frigate_file)
-                        progress.advance(upload_task)
-                        continue
+                        candidate_score = score_map.get(fname)
+                        if candidate_score is None:
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: no quality score, skipping replacement[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        target = get_lowest_quality_mapped_file(name, exclude=failed_deletes)
+                        if target is None or candidate_score <= target[2]:
+                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: blur {candidate_score:.3f} ≤ worst"
+                                f" {target_score_str}, skipping[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        target_frigate_file, _target_asset_id, target_score = target
+                        progress.console.print(
+                            f"    🔄 {fname}: blur {candidate_score:.3f} > {target_score:.3f},"
+                            f" replacing {target_frigate_file}"
+                        )
+                        if delete_frigate_person_files(name, [target_frigate_file]):
+                            remove_frigate_file(name, target_frigate_file)
+                            effective_count -= 1
+                            min_quality_score_for_slot = score_map.get(fname)
+                        else:
+                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
+                            failed_deletes.add(target_frigate_file)
+                            progress.advance(upload_task)
+                            continue
 
                 for attempt in range(1, max_retries + 1):
                     try:
@@ -424,7 +531,13 @@ def upload_to_frigate(jobs: list[dict]) -> None:
 
                             asset_id = asset_map.get(fname)
                             if asset_id:
-                                mark_uploaded(asset_id, person_name=name, score=score_map.get(fname))
+                                mark_uploaded(
+                                    asset_id,
+                                    person_name=name,
+                                    score=score_map.get(fname),
+                                    crop_dims=dims_map.get(fname),
+                                    frigate_score=pre_fscore,
+                                )
                                 actually_uploaded.append((fname, asset_id))
 
                             break

winnow/frigate_api.py

@@ -40,6 +40,22 @@ def get_frigate_face_counts() -> dict[str, int] | None:
     }
 
 
+def get_all_frigate_person_files() -> dict[str, list[str]] | None:
+    """Return {person_name: [filename, ...]} for every person in Frigate.
+
+    Single call used to build per-person snapshots before the upload loop,
+    avoiding one GET /api/faces per person.  Returns None if unavailable.
+    """
+    data = _get_faces_data()
+    if data is None:
+        return None
+    return {
+        name: files
+        for name, files in data.items()
+        if name != "train" and isinstance(files, list)
+    }
+
+
 def get_frigate_person_files(person_name: str) -> list[str] | None:
     """Return the list of training filenames for a person in Frigate.
 
@@ -53,6 +69,38 @@ def get_frigate_person_files(person_name: str) -> list[str] | None:
     return files if isinstance(files, list) else []
 
 
+def recognize_face(file_path: str) -> tuple[str | None, float] | None:
+    """Submit an image to Frigate's recognize endpoint.
+
+    Returns (face_name, score) where face_name is the best-matching person
+    (may be "unknown" if below Frigate's confidence threshold) and score is
+    the sigmoid-mapped cosine similarity (0-1) against that person's mean
+    embedding.
+
+    Returns None if FRIGATE_URL is unset, the API is unreachable, no face is
+    detected, or face recognition is not enabled in Frigate.
+    """
+    frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/")
+    if not frigate_url:
+        return None
+    try:
+        with open(file_path, "rb") as f:
+            resp = requests.post(
+                f"{frigate_url}/api/faces/recognize",
+                files={"file": (os.path.basename(file_path), f, "image/jpeg")},
+                timeout=15,
+            )
+        if not resp.ok:
+            return None
+        data = resp.json()
+        if data.get("success") and "score" in data:
+            return (data.get("face_name"), round(float(data["score"]), 4))
+        return None
+    except Exception as e:
+        logger.debug(f"Frigate recognize failed for {file_path}: {e}")
+        return None
+
+
 def delete_frigate_person_files(person_name: str, filenames: list[str]) -> bool:
     """Delete specific training files for a person from Frigate.
 

winnow/image_processing.py

@@ -69,9 +69,10 @@ def process_face_mode(
     output_dir: str,
     count: int,
     min_width: int | None = None,
-) -> bool:
+) -> tuple[int, int] | None:
     """Crop face based on Immich metadata and save to output directory.
 
+    Returns (width, height) of the saved crop, or None if no crop was saved.
     If face alignment is enabled and landmarks are available, produces
     an aligned 112x112 crop. Otherwise falls back to bounding box crop
     with configurable margin.
@@ -90,7 +91,7 @@ def process_face_mode(
 
     if not face_info:
         logger.debug(f"No face info for {person.get('name')} in asset {asset.get('id')}")
-        return False
+        return None
 
     img_w, img_h = img.size
     meta_w = face_info.get("imageWidth") or img_w
@@ -106,7 +107,7 @@ def process_face_mode(
     face_w, face_h = x2 - x1, y2 - y1
     if face_w < min_width or face_h < min_width:
         logger.debug(f"Face too small ({face_w:.1f}x{face_h:.1f})")
-        return False
+        return None
 
     # Try face alignment if enabled and landmarks available
     if Config.ENABLE_FACE_ALIGNMENT:
@@ -117,7 +118,7 @@ def process_face_mode(
             aligned = align_face(img, scaled_landmarks)
             if aligned is not None:
                 _save_jpeg(aligned, os.path.join(output_dir, f"{count}.jpg"))
-                return True
+                return aligned.size
 
     # Fall back to bounding box crop with configurable margin
     margin = Config.FACE_MARGIN
@@ -131,7 +132,7 @@ def process_face_mode(
 
     face_crop = img.crop(crop_box)
     _save_jpeg(face_crop, os.path.join(output_dir, f"{count}.jpg"))
-    return True
+    return face_crop.size
 
 
 def process_object_mode(

winnow/quality.py

@@ -119,7 +119,10 @@ def assess_quality(
     blur_score = float(cv2.Laplacian(gray, cv2.CV_64F).var())
 
     checks = [
-        (blur_score >= blur_threshold, f"Blurry (laplacian={blur_score:.1f}, threshold={blur_threshold})" if blur_score < blur_threshold else ""),
+        (
+            blur_score >= blur_threshold,
+            f"Blurry (laplacian={blur_score:.1f}, threshold={blur_threshold})" if blur_score < blur_threshold else "",
+        ),
         check_grayscale(img_np),
         check_exposure(img_np),
         check_confidence(confidence, min_confidence),

winnow/upload_tracker.py

@@ -11,12 +11,18 @@ Both are excluded from future candidate pools. To reset:
 
 by_person schema (frigate_uploaded_ids.json):
   {
-    "asset_ids":     ["immich-id-1", ...],                    # all assets we attempted to upload
-    "scores":        {"immich-id-1": 450.3},                   # Laplacian blur variance at upload time
-    "frigate_files": {"PersonName-123.webp": "immich-id-1"},  # Frigate filename → asset ID
-    "frigate_count": 42                                       # last known Frigate training image count
+    "asset_ids":      ["immich-id-1", ...],                    # all assets we attempted to upload
+    "scores":         {"immich-id-1": 450.3},                  # Laplacian blur variance at upload time
+    "frigate_scores": {"immich-id-1": 0.87},                   # Frigate recognition confidence (0-1) pre-upload
+    "frigate_files":  {"PersonName-123.webp": "immich-id-1"},  # Frigate filename → asset ID
+    "crop_dims":      {"immich-id-1": [640, 480]},             # crop pixel dimensions at upload time
+    "frigate_count":  42                                       # last known Frigate training image count
   }
 
+frigate_scores stores pre-upload recognize scores (0-1 sigmoid-mapped cosine
+similarity). High score = the existing training set already covers this face
+condition well. Low score = a gap — novel/diverse for the training set.
+
 frigate_files only contains files winnow uploaded — files added manually through
 Frigate's UI are never mapped here and are never touched by quality replacement.
 """
@@ -76,14 +82,23 @@ def _get_ids(entry: list | dict) -> list[str]:
 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": {}, "frigate_files": {}}
+        return {"asset_ids": sorted(entry), "scores": {}, "frigate_scores": {}, "frigate_files": {}, "crop_dims": {}}
     entry.setdefault("asset_ids", [])
     entry.setdefault("scores", {})
+    entry.setdefault("frigate_scores", {})
     entry.setdefault("frigate_files", {})
+    entry.setdefault("crop_dims", {})
     return entry
 
 
-def _mark(filename: str, asset_id: str, person_name: str | None, score: float | None = None) -> None:
+def _mark(
+    filename: str,
+    asset_id: str,
+    person_name: str | None,
+    score: float | None = None,
+    crop_dims: tuple[int, int] | None = None,
+    frigate_score: float | None = None,
+) -> None:
     data = _load(filename)
     flat_key = _flat_key(filename)
     flat = set(data.get(flat_key, []))
@@ -97,6 +112,10 @@ def _mark(filename: str, asset_id: str, person_name: str | None, score: float |
         entry["asset_ids"] = sorted(ids)
         if score is not None:
             entry["scores"][asset_id] = round(score, 4)
+        if crop_dims is not None:
+            entry["crop_dims"][asset_id] = [crop_dims[0], crop_dims[1]]
+        if frigate_score is not None:
+            entry["frigate_scores"][asset_id] = round(frigate_score, 4)
         by_person[person_name] = entry
     _save(filename, data)
 
@@ -111,8 +130,14 @@ def load_rejected_ids() -> set[str]:
     return _load_flat(REJECT_TRACKER_FILE)
 
 
-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)
+def mark_uploaded(
+    asset_id: str,
+    person_name: str | None = None,
+    score: float | None = None,
+    crop_dims: tuple[int, int] | None = None,
+    frigate_score: float | None = None,
+) -> None:
+    _mark(UPLOAD_TRACKER_FILE, asset_id, person_name, score=score, crop_dims=crop_dims, frigate_score=frigate_score)
     logger.debug(f"Marked {asset_id} as uploaded ({person_name})")
 
 
@@ -121,6 +146,7 @@ def mark_rejected(asset_id: str, person_name: str | None = None) -> None:
     logger.debug(f"Marked {asset_id} as rejected ({person_name})")
 
 
+
 def record_frigate_file(person_name: str, frigate_filename: str, asset_id: str) -> None:
     """Record the mapping from a Frigate training filename to an Immich asset ID."""
     data = _load(UPLOAD_TRACKER_FILE)
@@ -169,29 +195,107 @@ def get_tracked_frigate_filenames(person_name: str) -> set[str]:
     return set(entry["frigate_files"].keys())
 
 
+def has_frigate_scores(person_name: str) -> bool:
+    """Return True if any mapped file for this person has a stored Frigate recognition score."""
+    data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
+    frigate_files = entry.get("frigate_files", {})
+    frigate_scores = entry.get("frigate_scores", {})
+    return any(asset_id in frigate_scores for asset_id in frigate_files.values())
+
+
 def get_lowest_quality_mapped_file(
     person_name: str, exclude: set[str] | None = None
 ) -> tuple[str, str, float] | None:
     """Return (frigate_filename, asset_id, score) for the mapped file with the lowest
-    quality score, or None if no mapped files with known scores exist.
+    blur score, or None if no mapped files with known scores exist.
 
-    Pass `exclude` to skip files that failed to delete this run without removing
-    them from the tracker — they remain candidates on the next run.
+    Used for quality replacement when no Frigate scores are available.
+    Pass `exclude` to skip files that failed to delete this run.
     """
     data = _load(UPLOAD_TRACKER_FILE)
     entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
     frigate_files = entry.get("frigate_files", {})
-    scores = entry.get("scores", {})
+    blur_scores = entry.get("scores", {})
+
     candidates = [
-        (frigate_filename, asset_id, scores[asset_id])
-        for frigate_filename, asset_id in frigate_files.items()
-        if asset_id in scores and (exclude is None or frigate_filename not in exclude)
+        (ff, asset_id, blur_scores[asset_id])
+        for ff, asset_id in frigate_files.items()
+        if (exclude is None or ff not in exclude)
+        and asset_id in blur_scores
     ]
+
     if not candidates:
         return None
     return min(candidates, key=lambda x: x[2])
 
 
+def get_most_redundant_mapped_file(
+    person_name: str, exclude: set[str] | None = None
+) -> tuple[str, str, float] | None:
+    """Return (frigate_filename, asset_id, score) for the mapped file with the highest
+    Frigate recognition score, or None if no mapped files with Frigate scores exist.
+
+    High Frigate score = the training set already covers this face condition well
+    = the most redundant file and therefore the best replacement target.
+    Pass `exclude` to skip files that failed to delete this run.
+    """
+    data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
+    frigate_files = entry.get("frigate_files", {})
+    frigate_scores = entry.get("frigate_scores", {})
+
+    candidates = [
+        (ff, asset_id, frigate_scores[asset_id])
+        for ff, asset_id in frigate_files.items()
+        if (exclude is None or ff not in exclude)
+        and asset_id in frigate_scores
+    ]
+
+    if not candidates:
+        return None
+    return max(candidates, key=lambda x: x[2])
+
+
+def get_frigate_filename_for_asset(person_name: str, asset_id: str) -> str | None:
+    """Return the Frigate training filename mapped to this asset ID, or None."""
+    data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
+    for frigate_filename, aid in entry["frigate_files"].items():
+        if aid == asset_id:
+            return frigate_filename
+    return None
+
+
+def find_by_crop_dimension(size: int) -> list[dict]:
+    """Return all tracked crops whose width or height matches `size` pixels.
+
+    Returns a list of dicts: {person, asset_id, width, height, blur_score, frigate_filename}.
+    frigate_filename is None when the Frigate mapping was lost to a reconciliation race.
+    """
+    data = _load(UPLOAD_TRACKER_FILE)
+    results = []
+    for person_name, raw_entry in data.get("by_person", {}).items():
+        entry = _migrate_entry(raw_entry)
+        scores = entry.get("scores", {})
+        frigate_files = entry.get("frigate_files", {})
+        asset_to_frigate = {v: k for k, v in frigate_files.items()}
+        frigate_scores = entry.get("frigate_scores", {})
+        for asset_id, dims in entry.get("crop_dims", {}).items():
+            w, h = dims[0], dims[1]
+            if w == size or h == size:
+                results.append({
+                    "person": person_name,
+                    "asset_id": asset_id,
+                    "width": w,
+                    "height": h,
+                    "blur_score": scores.get(asset_id),
+                    "frigate_score": frigate_scores.get(asset_id),
+                    "frigate_filename": asset_to_frigate.get(asset_id),
+                })
+    return results
+
+
 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)