chore: bump version to 0.4.2, update changelog

CUDA base image downgraded to 12.8.1 (driver 570 compatibility fix),
benchmark script added.

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

@@ -2,7 +2,7 @@ name: Publish Docker Image
 
 on:
   push:
-    branches: ["main", "dev"]
+    branches: ["dev"]
     paths-ignore:
       - "**.md"
       - "docs/**"
@@ -15,6 +15,16 @@ on:
       - "uv-cpu.lock"
       - "uv-rocm.lock"
       - "uv-intel.lock"
+  workflow_call:
+    inputs:
+      tag:
+        type: string
+        required: false
+        description: "Release tag, e.g. v0.4.1 — triggers :latest + versioned image tags"
+      version:
+        type: string
+        required: false
+        description: "Version string without v prefix, e.g. 0.4.1"
 
 concurrency:
   group: docker-${{ github.ref }}
@@ -50,6 +60,8 @@ jobs:
 
       - name: Checkout repository
         uses: actions/checkout@v6
+        with:
+          ref: ${{ inputs.tag || github.ref }}
 
       - name: Set up QEMU
         if: matrix.platform == 'linux/arm64'
@@ -65,6 +77,15 @@ jobs:
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
+      - name: Compute build version
+        id: version
+        run: |
+          if [ -n "${{ inputs.version }}" ]; then
+            echo "value=${{ inputs.version }}" >> "$GITHUB_OUTPUT"
+          else
+            echo "value=dev" >> "$GITHUB_OUTPUT"
+          fi
+
       - name: Build and push by digest
         id: build
         uses: docker/build-push-action@v7
@@ -72,6 +93,7 @@ jobs:
           context: .
           file: ./Dockerfile
           platforms: ${{ matrix.platform }}
+          build-args: VERSION=${{ steps.version.outputs.value }}
           cache-from: type=gha,scope=${{ matrix.platform }}
           cache-to: type=gha,mode=max,scope=${{ matrix.platform }}
           github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -120,22 +142,26 @@ jobs:
       - name: Determine image tags
         id: tags
         run: |
-          if [ "${{ github.ref_name }}" = "dev" ]; then
-            echo "tags=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:dev" >> "$GITHUB_OUTPUT"
+          IMAGE="${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}"
+          INPUT_TAG="${{ inputs.tag }}"
+          if [ -n "$INPUT_TAG" ]; then
+            echo "tag_args=-t ${IMAGE}:latest -t ${IMAGE}:${INPUT_TAG}" >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:latest" >> "$GITHUB_OUTPUT"
           else
-            echo "tags=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:latest" >> "$GITHUB_OUTPUT"
+            echo "tag_args=-t ${IMAGE}:dev" >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:dev" >> "$GITHUB_OUTPUT"
           fi
 
       - name: Create and push multi-arch manifest
         working-directory: /tmp/digests
         run: |
           docker buildx imagetools create \
-            -t ${{ steps.tags.outputs.tags }} \
+            ${{ steps.tags.outputs.tag_args }} \
             $(printf '${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}@sha256:%s ' *)
 
       - name: Inspect image
         run: |
-          docker buildx imagetools inspect ${{ steps.tags.outputs.tags }}
+          docker buildx imagetools inspect ${{ steps.tags.outputs.inspect_tag }}
 
       - name: Ensure package is public
         run: |
@@ -162,6 +188,8 @@ jobs:
 
       - name: Checkout repository
         uses: actions/checkout@v6
+        with:
+          ref: ${{ inputs.tag || github.ref }}
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v4
@@ -176,13 +204,30 @@ jobs:
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Determine CPU image tag
-        id: cpu-tag
+      - name: Determine CPU image tags
+        id: cpu-tags
         run: |
-          if [ "${{ github.ref_name }}" = "dev" ]; then
-            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:dev-cpu" >> "$GITHUB_OUTPUT"
+          IMAGE="${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}"
+          INPUT_TAG="${{ inputs.tag }}"
+          if [ -n "$INPUT_TAG" ]; then
+            {
+              echo "tags<<EOF"
+              printf '%s\n' "${IMAGE}:cpu" "${IMAGE}:${INPUT_TAG}-cpu"
+              echo "EOF"
+            } >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:cpu" >> "$GITHUB_OUTPUT"
           else
-            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:cpu" >> "$GITHUB_OUTPUT"
+            echo "tags=${IMAGE}:dev-cpu" >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:dev-cpu" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Compute build version
+        id: version
+        run: |
+          if [ -n "${{ inputs.version }}" ]; then
+            echo "value=${{ inputs.version }}" >> "$GITHUB_OUTPUT"
+          else
+            echo "value=dev" >> "$GITHUB_OUTPUT"
           fi
 
       - name: Build and push CPU image
@@ -191,16 +236,18 @@ jobs:
           context: .
           file: ./Dockerfile
           platforms: linux/amd64,linux/arm64
-          build-args: VARIANT=cpu
+          build-args: |
+            VARIANT=cpu
+            VERSION=${{ steps.version.outputs.value }}
           cache-from: type=gha,scope=cpu
           cache-to: type=gha,mode=max,scope=cpu
           github-token: ${{ secrets.GITHUB_TOKEN }}
           push: true
-          tags: ${{ steps.cpu-tag.outputs.tag }}
+          tags: ${{ steps.cpu-tags.outputs.tags }}
 
       - name: Inspect CPU image
         run: |
-          docker buildx imagetools inspect ${{ steps.cpu-tag.outputs.tag }}
+          docker buildx imagetools inspect ${{ steps.cpu-tags.outputs.inspect_tag }}
 
       - name: Ensure package is public
         run: |
@@ -227,6 +274,8 @@ jobs:
 
       - name: Checkout repository
         uses: actions/checkout@v6
+        with:
+          ref: ${{ inputs.tag || github.ref }}
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v4
@@ -238,13 +287,30 @@ jobs:
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Determine ROCm image tag
-        id: rocm-tag
+      - name: Determine ROCm image tags
+        id: rocm-tags
         run: |
-          if [ "${{ github.ref_name }}" = "dev" ]; then
-            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:dev-rocm" >> "$GITHUB_OUTPUT"
+          IMAGE="${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}"
+          INPUT_TAG="${{ inputs.tag }}"
+          if [ -n "$INPUT_TAG" ]; then
+            {
+              echo "tags<<EOF"
+              printf '%s\n' "${IMAGE}:rocm" "${IMAGE}:${INPUT_TAG}-rocm"
+              echo "EOF"
+            } >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:rocm" >> "$GITHUB_OUTPUT"
           else
-            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:rocm" >> "$GITHUB_OUTPUT"
+            echo "tags=${IMAGE}:dev-rocm" >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:dev-rocm" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Compute build version
+        id: version
+        run: |
+          if [ -n "${{ inputs.version }}" ]; then
+            echo "value=${{ inputs.version }}" >> "$GITHUB_OUTPUT"
+          else
+            echo "value=dev" >> "$GITHUB_OUTPUT"
           fi
 
       - name: Build and push ROCm image
@@ -253,16 +319,18 @@ jobs:
           context: .
           file: ./Dockerfile
           platforms: linux/amd64
-          build-args: VARIANT=rocm
+          build-args: |
+            VARIANT=rocm
+            VERSION=${{ steps.version.outputs.value }}
           cache-from: type=gha,scope=linux/amd64-rocm
           cache-to: type=gha,mode=max,scope=linux/amd64-rocm
           github-token: ${{ secrets.GITHUB_TOKEN }}
           push: true
-          tags: ${{ steps.rocm-tag.outputs.tag }}
+          tags: ${{ steps.rocm-tags.outputs.tags }}
 
       - name: Inspect ROCm image
         run: |
-          docker buildx imagetools inspect ${{ steps.rocm-tag.outputs.tag }}
+          docker buildx imagetools inspect ${{ steps.rocm-tags.outputs.inspect_tag }}
 
       - name: Ensure package is public
         run: |
@@ -289,6 +357,8 @@ jobs:
 
       - name: Checkout repository
         uses: actions/checkout@v6
+        with:
+          ref: ${{ inputs.tag || github.ref }}
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v4
@@ -300,13 +370,30 @@ jobs:
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Determine Intel image tag
-        id: intel-tag
+      - name: Determine Intel image tags
+        id: intel-tags
         run: |
-          if [ "${{ github.ref_name }}" = "dev" ]; then
-            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:dev-intel" >> "$GITHUB_OUTPUT"
+          IMAGE="${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}"
+          INPUT_TAG="${{ inputs.tag }}"
+          if [ -n "$INPUT_TAG" ]; then
+            {
+              echo "tags<<EOF"
+              printf '%s\n' "${IMAGE}:intel" "${IMAGE}:${INPUT_TAG}-intel"
+              echo "EOF"
+            } >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:intel" >> "$GITHUB_OUTPUT"
           else
-            echo "tag=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:intel" >> "$GITHUB_OUTPUT"
+            echo "tags=${IMAGE}:dev-intel" >> "$GITHUB_OUTPUT"
+            echo "inspect_tag=${IMAGE}:dev-intel" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Compute build version
+        id: version
+        run: |
+          if [ -n "${{ inputs.version }}" ]; then
+            echo "value=${{ inputs.version }}" >> "$GITHUB_OUTPUT"
+          else
+            echo "value=dev" >> "$GITHUB_OUTPUT"
           fi
 
       - name: Build and push Intel image
@@ -315,16 +402,18 @@ jobs:
           context: .
           file: ./Dockerfile
           platforms: linux/amd64
-          build-args: VARIANT=intel
+          build-args: |
+            VARIANT=intel
+            VERSION=${{ steps.version.outputs.value }}
           cache-from: type=gha,scope=linux/amd64-intel
           cache-to: type=gha,mode=max,scope=linux/amd64-intel
           github-token: ${{ secrets.GITHUB_TOKEN }}
           push: true
-          tags: ${{ steps.intel-tag.outputs.tag }}
+          tags: ${{ steps.intel-tags.outputs.tags }}
 
       - name: Inspect Intel image
         run: |
-          docker buildx imagetools inspect ${{ steps.intel-tag.outputs.tag }}
+          docker buildx imagetools inspect ${{ steps.intel-tags.outputs.inspect_tag }}
 
       - name: Ensure package is public
         run: |

.github/workflows/release.yml

@@ -127,188 +127,14 @@ jobs:
               prerelease: false,
             });
 
-  build-gpu:
-    name: Build GPU image
+  build-images:
+    name: Build and push Docker images
     needs: release
-    runs-on: ubuntu-latest
+    uses: ./.github/workflows/docker-publish.yml
+    with:
+      tag: ${{ needs.release.outputs.tag }}
+      version: ${{ needs.release.outputs.version }}
+    secrets: inherit
     permissions:
       packages: write
-    steps:
-      - name: Free up disk space
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf "/usr/local/share/boost"
-          sudo rm -rf "$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: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build and push GPU image (latest)
-        uses: docker/build-push-action@v7
-        with:
-          context: .
-          file: ./Dockerfile
-          platforms: linux/amd64,linux/arm64
-          push: true
-          build-args: VERSION=${{ 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:${{ needs.release.outputs.tag }}
-
-  build-cpu:
-    name: Build CPU image
-    needs: release
-    runs-on: ubuntu-latest
-    permissions:
-      packages: write
-    steps:
-      - name: Free up disk space
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf "/usr/local/share/boost"
-          sudo rm -rf "$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: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build and push CPU image
-        uses: docker/build-push-action@v7
-        with:
-          context: .
-          file: ./Dockerfile
-          platforms: linux/amd64,linux/arm64
-          push: true
-          build-args: |
-            VARIANT=cpu
-            VERSION=${{ 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:${{ needs.release.outputs.tag }}-cpu
-
-  build-rocm:
-    name: Build ROCm image
-    needs: release
-    runs-on: ubuntu-latest
-    permissions:
-      packages: write
-    steps:
-      - name: Free up disk space
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf "/usr/local/share/boost"
-          sudo rm -rf "$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: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build and push ROCm image
-        uses: docker/build-push-action@v7
-        with:
-          context: .
-          file: ./Dockerfile
-          platforms: linux/amd64
-          push: true
-          build-args: |
-            VARIANT=rocm
-            VERSION=${{ 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:${{ needs.release.outputs.tag }}-rocm
-
-  build-intel:
-    name: Build Intel image
-    needs: release
-    runs-on: ubuntu-latest
-    permissions:
-      packages: write
-    steps:
-      - name: Free up disk space
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /opt/ghc
-          sudo rm -rf "/usr/local/share/boost"
-          sudo rm -rf "$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: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Build and push Intel image
-        uses: docker/build-push-action@v7
-        with:
-          context: .
-          file: ./Dockerfile
-          platforms: linux/amd64
-          push: true
-          build-args: |
-            VARIANT=intel
-            VERSION=${{ 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:${{ needs.release.outputs.tag }}-intel
+      contents: read

CHANGELOG.md

@@ -7,6 +7,60 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.4.2] - 2026-06-13
+
+### Changed
+
+- **GPU image now uses CUDA 12.8.1** (was 13.3): CUDA 13.3 requires driver ≥ 575; driver 570 (the current stable release) was incorrectly rejected with "CUDA driver version is insufficient" at startup. The `:latest` image now works with any NVIDIA driver ≥ 570.
+
+### Added
+
+- **`scripts/benchmark.py`**: measures InsightFace and SigLIP inference latency and throughput across GPU and CPU modes. Run inside the container with `python /app/scripts/benchmark.py`. RTX 2070 SUPER results: InsightFace 12.8 ms / 78 img/s (8× CPU), SigLIP batch 32 at 5.4 ms/img / 187 img/s (33× CPU).
+
+## [0.4.1] - 2026-06-13
+
+### Fixed
+
+- **`RESET_PERSON` no longer creates duplicate Frigate files**: previously, resetting a person only wiped the local tracker — existing Frigate training files were left as unmanaged orphans, causing the next run to upload a full new batch on top of them. `reset_person` now deletes all winnow-managed files for that person from Frigate before clearing the tracker. Manually-added Frigate files are unaffected.
+- **No spurious warning when `FRIGATE_URL` is unset and `RESET_PERSON` is used**: the deletion step is now skipped silently at info level rather than logging a misleading "could not delete" warning.
+
+## [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

Dockerfile

@@ -1,5 +1,5 @@
 # ── Base images ───────────────────────────────────────────────────────────────
-#   amd64 + gpu:   NVIDIA CUDA 13.3 + cuDNN (GPU acceleration via NVIDIA Container Toolkit)
+#   amd64 + gpu:   NVIDIA CUDA 12.8 + cuDNN (GPU acceleration via NVIDIA Container Toolkit)
 #   amd64 + rocm:  Ubuntu 22.04 (AMD GPU via ROCm — pass /dev/kfd and /dev/dri)
 #   amd64 + intel: Ubuntu 22.04 (Intel Arc / iGPU via OpenVINO — pass /dev/dri)
 #   amd64 + cpu:   Ubuntu 22.04 (CPU-only, ~2 GB smaller image)
@@ -7,7 +7,7 @@
 
 ARG VARIANT=gpu
 
-FROM --platform=$BUILDPLATFORM nvidia/cuda:13.3.0-cudnn-runtime-ubuntu22.04 AS base-amd64-gpu
+FROM --platform=$BUILDPLATFORM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04 AS base-amd64-gpu
 FROM ubuntu:22.04 AS base-amd64-rocm
 FROM ubuntu:22.04 AS base-amd64-intel
 FROM ubuntu:22.04 AS base-amd64-cpu

README.md

@@ -2,12 +2,17 @@
 
 [![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.
 
 Frigate's face recognition is only as good as its training data — and the key quality metric is **diversity**, not volume. A hundred photos from the same week teach the model one lighting condition. What you need is a spread: different years, different angles, different lighting, different contexts. Your photo library already has that data. winnow finds and delivers the right subset automatically.
 
+> **winnow only touches files it uploaded.** Faces added to Frigate manually through its UI are never deleted, replaced, or modified — not by quality replacement, not by `RESET_PERSON`, not by stale cleanup. If you have a curated training set you want to keep, it is safe.
+
 ---
 
 ## How It Works
@@ -56,9 +61,11 @@ 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 +190,16 @@ 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
 
@@ -215,7 +224,7 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 | :--- | :--- | :--- |
 | `DRY_RUN` | `false` | Preview selection without downloading or uploading |
 | `RETRY_REJECTED` | `false` | Re-attempt assets previously rejected by Frigate |
-| `RESET_PERSON` | *(unset)* | Clear upload and rejection history for one person by name |
+| `RESET_PERSON` | *(unset)* | Clear upload history for one person and delete their winnow-managed Frigate training files so the next run starts fresh. Manually added Frigate files are never touched |
 
 ### Scheduling
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.3.2"
+version = "0.4.2"
 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"

scripts/benchmark.py

@@ -0,0 +1,196 @@
+#!/usr/bin/env python3
+"""
+winnow inference benchmark: GPU vs CPU throughput.
+
+Measures InsightFace (face mode) and SigLIP (object mode) latency and
+throughput. Run with FORCE_CPU=true for CPU-only baseline.
+
+Usage inside container:
+    # GPU mode:
+    docker exec winnow python /app/scripts/benchmark.py
+
+    # CPU mode:
+    docker exec -e FORCE_CPU=true winnow python /app/scripts/benchmark.py
+"""
+
+import os
+import sys
+import time
+
+import numpy as np
+from PIL import Image, ImageDraw
+
+
+def _mode_label() -> str:
+    if os.getenv("FORCE_CPU", "").lower() in ("true", "1", "yes"):
+        return "CPU (FORCE_CPU=true)"
+    return "GPU (auto)"
+
+
+def make_face_image(size: int = 640) -> Image.Image:
+    """Synthetic face-like image: skin-tone rectangle with landmark blobs."""
+    img = Image.new("RGB", (size, size), (200, 170, 140))
+    draw = ImageDraw.Draw(img)
+    # Head oval
+    cx, cy = size // 2, size // 2
+    hw, hh = int(size * 0.3), int(size * 0.38)
+    draw.ellipse([cx - hw, cy - hh, cx + hw, cy + hh], fill=(220, 185, 155))
+    # Eyes
+    for ex in [cx - int(size * 0.1), cx + int(size * 0.1)]:
+        ey = cy - int(size * 0.05)
+        r = max(4, size // 40)
+        draw.ellipse([ex - r, ey - r, ex + r, ey + r], fill=(40, 30, 20))
+    # Nose
+    draw.ellipse([cx - 5, cy + 5, cx + 5, cy + 15], fill=(180, 140, 110))
+    # Mouth
+    draw.arc([cx - 20, cy + 25, cx + 20, cy + 45], start=0, end=180, fill=(160, 80, 80), width=3)
+    return img
+
+
+def make_random_image(width: int = 224, height: int = 224) -> Image.Image:
+    rng = np.random.default_rng(42)
+    return Image.fromarray(rng.integers(0, 256, (height, width, 3), dtype=np.uint8), "RGB")
+
+
+def _stats(times_s: list[float]) -> dict:
+    arr = np.array(times_s) * 1000  # ms
+    return {
+        "median_ms": float(np.median(arr)),
+        "mean_ms": float(np.mean(arr)),
+        "min_ms": float(np.min(arr)),
+        "p95_ms": float(np.percentile(arr, 95)),
+        "ips": 1000.0 / float(np.median(arr)),
+    }
+
+
+def bench_insightface(n_warmup: int = 5, n_runs: int = 30) -> None:
+    import cv2
+
+    import winnow.embeddings as emb_mod
+    from winnow.embeddings import get_insightface_app
+
+    # Reset singleton so we get a fresh load
+    emb_mod._insightface_app = None
+    emb_mod._insightface_loaded = False
+
+    print("  Loading model...")
+    t_load = time.perf_counter()
+    app = get_insightface_app()
+    load_s = time.perf_counter() - t_load
+
+    if app is None:
+        print("  SKIP: InsightFace failed to load")
+        return
+
+    img_pil = make_face_image(640)
+    img_bgr = cv2.cvtColor(np.asarray(img_pil), cv2.COLOR_RGB2BGR)
+
+    # Warmup
+    for _ in range(n_warmup):
+        app.get(img_bgr)
+
+    # Timed — single image 640×640
+    times: list[float] = []
+    for _ in range(n_runs):
+        t0 = time.perf_counter()
+        app.get(img_bgr)
+        times.append(time.perf_counter() - t0)
+
+    s = _stats(times)
+    print(f"  Model load time : {load_s:.2f} s")
+    print("  Input size      : 640×640")
+    print(f"  Runs            : {n_runs} (after {n_warmup} warmup)")
+    print(f"  Median latency  : {s['median_ms']:.1f} ms")
+    print(f"  Mean / p95      : {s['mean_ms']:.1f} ms / {s['p95_ms']:.1f} ms")
+    print(f"  Min latency     : {s['min_ms']:.1f} ms")
+    print(f"  Throughput      : {s['ips']:.1f} images/s")
+
+    # Also test at 320×320
+    img_sm = make_face_image(320)
+    img_sm_bgr = cv2.cvtColor(np.asarray(img_sm), cv2.COLOR_RGB2BGR)
+    for _ in range(n_warmup):
+        app.get(img_sm_bgr)
+    times_sm: list[float] = []
+    for _ in range(n_runs):
+        t0 = time.perf_counter()
+        app.get(img_sm_bgr)
+        times_sm.append(time.perf_counter() - t0)
+    s2 = _stats(times_sm)
+    print(f"  320×320 median  : {s2['median_ms']:.1f} ms  ({s2['ips']:.1f} img/s)")
+
+
+def bench_siglip(
+    n_warmup: int = 3,
+    n_runs: int = 20,
+    batch_sizes: tuple = (1, 4, 8, 16, 32),
+) -> None:
+    import torch
+
+    import winnow.embeddings as emb_mod
+    emb_mod._siglip_model = None
+    emb_mod._siglip_processor = None
+    emb_mod._siglip_loaded = False
+
+    print("  Loading model...")
+    t_load = time.perf_counter()
+    model, processor = emb_mod.get_siglip_model()
+    load_s = time.perf_counter() - t_load
+
+    if model is None:
+        print("  SKIP: SigLIP failed to load")
+        return
+
+    device = next(model.parameters()).device
+    print(f"  Model load time : {load_s:.2f} s  (device: {device})")
+
+    print(f"  {'Batch':>5}  {'ms/batch':>10}  {'ms/img':>8}  {'img/s':>8}  {'p95/img':>9}")
+    for bs in batch_sizes:
+        imgs = [make_random_image(224, 224) for _ in range(bs)]
+        inputs = processor(images=imgs, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+
+        # Warmup
+        for _ in range(n_warmup):
+            with torch.no_grad():
+                model(**inputs)
+        if str(device) != "cpu":
+            torch.cuda.synchronize()
+
+        times: list[float] = []
+        for _ in range(n_runs):
+            if str(device) != "cpu":
+                torch.cuda.synchronize()
+            t0 = time.perf_counter()
+            with torch.no_grad():
+                model(**inputs)
+            if str(device) != "cpu":
+                torch.cuda.synchronize()
+            times.append(time.perf_counter() - t0)
+
+        s = _stats(times)
+        print(
+            f"  {bs:>5}  {s['median_ms']:>10.1f}  {s['median_ms']/bs:>8.2f}"
+            f"  {bs * 1000 / s['median_ms']:>8.1f}  {s['p95_ms']/bs:>9.2f}"
+        )
+
+
+def main() -> None:
+    print("=" * 56)
+    print("  winnow inference benchmark")
+    print(f"  Mode: {_mode_label()}")
+    print("=" * 56)
+    print()
+
+    print("── InsightFace Buffalo_L  (face detection + ArcFace) ──")
+    bench_insightface()
+    print()
+
+    print("── SigLIP google/siglip-base-patch16-224  (objects) ───")
+    bench_siglip()
+    print()
+
+
+if __name__ == "__main__":
+    # Add winnow to path when run directly inside container
+    sys.path.insert(0, "/app")
+    main()

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.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 = "0.3.2"
+version = "0.4.1"
 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" },

winnow/cli.py

@@ -41,6 +41,8 @@ def _handle_trace_crop(size_str: str) -> None:
             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:

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 +13,22 @@ 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,
@@ -182,6 +189,17 @@ 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:
@@ -305,6 +323,10 @@ 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}"),
@@ -342,7 +364,10 @@ 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
@@ -354,11 +379,28 @@ 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
+            person_has_fscores: bool = has_frigate_scores(name)
 
             for fname in person_files:
                 fpath = os.path.join(person_dir, fname)
@@ -378,40 +420,109 @@ 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 person_has_fscores:
+                        _result = recognize_face(fpath)
+                        if _result is not None and (_result[0] or "").casefold() == name.casefold():
+                            pre_fscore = _result[1]
+
+                # Ceiling check: skip if the existing training set already covers this
+                # face condition well. Applies below cap only — at cap, replacement logic
+                # drives the decision.
+                if not at_cap and Config.FRIGATE_SCORE_CEILING > 0 and pre_run_count > 0:
+                    if pre_fscore is not None and pre_fscore > Config.FRIGATE_SCORE_CEILING:
+                        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 = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
+                    if using_fscore:
+                        candidate_score = pre_fscore
+                        if candidate_score is None:
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: Frigate recognize unavailable, skipping replacement[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        # Low score = more novel than the most redundant mapped file = replace
+                        target = get_most_redundant_mapped_file(name, exclude=failed_deletes)
+                        if target is None or candidate_score >= target[2]:
+                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: frigate {candidate_score:.3f} ≥ most redundant"
+                                f" {target_score_str}, not more novel[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        target_frigate_file, _target_asset_id, target_score = target
                         progress.console.print(
-                            f"    [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)
+                            person_has_fscores = has_frigate_scores(name)
+                            effective_count -= 1
+                            # clear any blur-mode slot floor — Frigate uses a different score metric
+                            min_quality_score_for_slot = None
+                        else:
+                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
+                            failed_deletes.add(target_frigate_file)
+                            progress.advance(upload_task)
+                            continue
                     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)
+                            person_has_fscores = has_frigate_scores(name)
+                            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:
@@ -434,7 +545,10 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                     person_name=name,
                                     score=score_map.get(fname),
                                     crop_dims=dims_map.get(fname),
+                                    frigate_score=pre_fscore,
                                 )
+                                if pre_fscore is not None:
+                                    person_has_fscores = True
                                 actually_uploaded.append((fname, asset_id))
 
                             break

winnow/frigate_api.py

@@ -22,11 +22,11 @@ def _get_faces_data() -> dict | None:
         return None
 
 
-def get_frigate_face_counts() -> dict[str, int] | None:
-    """Return {person_name: training_image_count} from Frigate's train directory.
+def get_all_frigate_person_files() -> dict[str, list[str]] | None:
+    """Return {person_name: [filename, ...]} for every person in Frigate.
 
-    Returns None if FRIGATE_URL is not set or the API is unreachable, so callers
-    can distinguish "API unavailable" from "person has 0 images."
+    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:
@@ -34,12 +34,24 @@ def get_frigate_face_counts() -> dict[str, int] | None:
     # Response: {person_name: [file, ...], "train": [...], ...}
     # "train" is a flat pending list, not a person — skip it.
     return {
-        name: len(files)
+        name: files
         for name, files in data.items()
         if name != "train" and isinstance(files, list)
     }
 
 
+def get_frigate_face_counts() -> dict[str, int] | None:
+    """Return {person_name: training_image_count} from Frigate's train directory.
+
+    Returns None if FRIGATE_URL is not set or the API is unreachable, so callers
+    can distinguish "API unavailable" from "person has 0 images."
+    """
+    all_files = get_all_frigate_person_files()
+    if all_files is None:
+        return None
+    return {name: len(files) for name, files in all_files.items()}
+
+
 def get_frigate_person_files(person_name: str) -> list[str] | None:
     """Return the list of training filenames for a person in Frigate.
 
@@ -53,6 +65,38 @@ 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/upload_tracker.py

@@ -11,21 +11,29 @@ 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
-    "crop_dims":     {"immich-id-1": [640, 480]},             # crop pixel dimensions at upload time
-    "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.
 """
 
 import json
 import logging
+import os
 from pathlib import Path
 
+from .frigate_api import delete_frigate_person_files
+
 logger = logging.getLogger(__name__)
 
 UPLOAD_TRACKER_FILE = "frigate_uploaded_ids.json"
@@ -77,9 +85,10 @@ 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": {}, "crop_dims": {}}
+        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
@@ -91,6 +100,7 @@ def _mark(
     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)
@@ -107,6 +117,8 @@ def _mark(
             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)
 
@@ -126,8 +138,9 @@ def mark_uploaded(
     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)
+    _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})")
 
 
@@ -136,6 +149,7 @@ 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)
@@ -156,7 +170,9 @@ def remove_frigate_file(person_name: str, frigate_filename: str) -> None:
     data = _load(UPLOAD_TRACKER_FILE)
     by_person = data.get("by_person", {})
     entry = _migrate_entry(by_person.get(person_name, {}))
-    entry["frigate_files"].pop(frigate_filename, None)
+    asset_id = entry["frigate_files"].pop(frigate_filename, None)
+    if asset_id:
+        entry["frigate_scores"].pop(asset_id, None)
     by_person[person_name] = entry
     _save(UPLOAD_TRACKER_FILE, data)
     logger.debug(f"Removed Frigate file mapping {frigate_filename} ({person_name})")
@@ -184,27 +200,64 @@ 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 _pick_mapped_file(
+    person_name: str, score_key: str, *, highest: bool, exclude: set[str] | None = None
+) -> tuple[str, str, float] | None:
+    data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
+    scores = entry.get(score_key, {})
+    candidates = [
+        (ff, asset_id, scores[asset_id])
+        for ff, asset_id in entry.get("frigate_files", {}).items()
+        if (exclude is None or ff not in exclude) and asset_id in scores
+    ]
+    if not candidates:
+        return None
+    return max(candidates, key=lambda x: x[2]) if highest else min(candidates, key=lambda x: x[2])
+
+
 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.
     """
+    return _pick_mapped_file(person_name, "scores", highest=False, exclude=exclude)
+
+
+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.
+    """
+    return _pick_mapped_file(person_name, "frigate_scores", highest=True, exclude=exclude)
+
+
+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, {}))
-    frigate_files = entry.get("frigate_files", {})
-    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)
-    ]
-    if not candidates:
-        return None
-    return min(candidates, key=lambda x: x[2])
+    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]:
@@ -220,6 +273,7 @@ def find_by_crop_dimension(size: int) -> list[dict]:
         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:
@@ -229,6 +283,7 @@ def find_by_crop_dimension(size: int) -> list[dict]:
                     "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
@@ -245,19 +300,41 @@ def update_frigate_count(person_name: str, count: int) -> None:
 
 
 def reset_person(person_name: str) -> None:
-    """Remove all uploaded and rejected records for a given person."""
-    for filename in (UPLOAD_TRACKER_FILE, REJECT_TRACKER_FILE):
-        data = _load(filename)
+    """Remove all uploaded and rejected records for a given person.
+
+    Also deletes winnow-managed Frigate training files so the next run starts
+    clean rather than uploading on top of orphaned files. Manually-added Frigate
+    files (not in frigate_files) are never touched. Proceeds with tracker reset
+    even if Frigate is unreachable.
+    """
+    upload_data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(upload_data.get("by_person", {}).get(person_name, {}))
+    frigate_filenames = list(entry.get("frigate_files", {}).keys())
+    if frigate_filenames:
+        if not os.environ.get("FRIGATE_URL", "").strip():
+            logger.info(f"FRIGATE_URL not set — skipping Frigate file deletion for {person_name}")
+        elif delete_frigate_person_files(person_name, frigate_filenames):
+            logger.info(f"Deleted {len(frigate_filenames)} Frigate file(s) for {person_name}")
+        else:
+            logger.warning(f"Could not delete Frigate files for {person_name} — tracker reset proceeding anyway")
+
+    changed = False
+    tracker_files = ((UPLOAD_TRACKER_FILE, upload_data), (REJECT_TRACKER_FILE, _load(REJECT_TRACKER_FILE)))
+    for filename, data in tracker_files:
         flat_key = _flat_key(filename)
         by_person = data.get("by_person", {})
-        entry = by_person.pop(person_name, None)
-        if entry is not None:
-            person_ids = set(_get_ids(entry))
+        tracker_entry = by_person.pop(person_name, None)
+        if tracker_entry is not None:
+            person_ids = set(_get_ids(tracker_entry))
             flat = set(data.get(flat_key, [])) - person_ids
             data[flat_key] = sorted(flat)
             data["by_person"] = by_person
             _save(filename, data)
-    logger.info(f"Reset tracking data for {person_name}")
+            changed = True
+    if changed:
+        logger.info(f"Reset tracking data for {person_name}")
+    else:
+        logger.debug(f"reset_person: no tracking data found for {person_name}")
 
 
 def get_person_summary() -> dict[str, dict]: