feat: remove object mode pipeline (YOLO, SigLIP, TRAINING_MODE, OBJECT_CLASS)
winnow is a face recognition training tool. Object mode required manual file placement with no Frigate API, pulled in torch/torchvision/transformers/ ultralytics (~2 GB), and was architecturally misaligned with the project goal. Removed: - process_object_mode (YOLO inference), get_yolo_model - SigLIP model stack (get_siglip_model, get_object_embedding, batch variant) - entity_type branching throughout diversity, embeddings, jobs, executor - TRAINING_MODE and OBJECT_CLASS env vars - torch, torchvision, transformers, ultralytics dependencies - pytorch index entries from pyproject.toml - Object mode from README (Modes section, env var table, How It Works)
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@@ -39,8 +39,7 @@ Immich library
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│
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▼
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4. Compute embeddings from the same preview thumbnails
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• Faces → InsightFace (ArcFace / Buffalo_L) → 512-dim vector
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• Objects → SigLIP (Vision Transformer) → 768-dim vector
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• InsightFace (ArcFace / Buffalo_L) → 512-dim vector
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│
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▼
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5. Near-duplicate removal — greedy cosine-distance pass drops burst shots
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@@ -61,35 +60,25 @@ Immich library
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7. Download full-resolution originals from Immich
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│
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▼
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8. Crop and process
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• Face mode: EXIF-corrected, landmark-aligned 112×112 crop (ArcFace format)
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• Object mode: YOLOv9c detection → one crop per matched instance
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8. Crop and process — EXIF-corrected, landmark-aligned 112×112 crop (ArcFace format)
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│
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▼
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9. Deliver
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• Face mode: upload crops to Frigate's face registration API
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↳ below MAX_AUTO_IMAGES — upload, unless the novelty gate
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(FRIGATE_SCORE_CEILING) determines the candidate is already
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covered by the current training set
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↳ at cap + QUALITY_REPLACEMENT=true — with Frigate scoring active,
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swap the most redundant tracked image (highest pre-upload recognize
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score) if the candidate is more novel (lower score); falling back to
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blur-score comparison when no Frigate scores are available; manually
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added files are never touched
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↳ at cap + QUALITY_REPLACEMENT=false — skip this person
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• Object mode: save crops to disk → place into your Frigate data directory
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9. Deliver — upload crops to Frigate's face registration API
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↳ below MAX_AUTO_IMAGES — upload, unless the novelty gate
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(FRIGATE_SCORE_CEILING) determines the candidate is already
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covered by the current training set
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↳ at cap + QUALITY_REPLACEMENT=true — with Frigate scoring active,
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swap the most redundant tracked image (highest pre-upload recognize
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score) if the candidate is more novel (lower score); falling back to
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blur-score comparison when no Frigate scores are available; manually
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added files are never touched
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↳ at cap + QUALITY_REPLACEMENT=false — skip this person
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```
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Uploaded and rejected asset IDs are persisted across runs. The same image is never processed twice; rejected assets are permanently skipped unless `RETRY_REJECTED=true`.
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---
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## Modes
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**Face mode** (default) — extracts face crops using Immich's bounding box metadata, applies EXIF orientation correction, and aligns them to ArcFace's standard 112×112 format using 5-point facial landmarks. Crops are uploaded directly to Frigate's face registration API.
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**Object mode** — runs each full-resolution image through YOLOv9c to detect instances of a target class (dog, cat, car, etc.), crops each detection, and saves it to the output directory. Frigate has no API for uploading object training data; place the crops into your Frigate data directory manually.
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---
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## Running in Docker
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@@ -116,7 +105,7 @@ services:
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- FRIGATE_URL=http://192.168.1.10:5000
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- CRON_SCHEDULE=0 3 * * 0
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volumes:
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- /path/to/models:/models
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- /path/to/models:/models # INSIGHTFACE_HOME — persists Buffalo_L model (~300 MB)
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- /path/to/cache:/app/.if_cache
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- /path/to/output:/app/frigate_train
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deploy:
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@@ -162,7 +151,7 @@ See [compose.yml](compose.yml) for the full annotated example with all options.
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| *(empty string)* | Stay alive, run nothing — trigger manually with `docker exec -it winnow winnow` |
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| Cron expression | Run on startup, then repeat on schedule |
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In scheduled mode the process (and loaded models) stays resident between runs. The first run after a fresh install downloads the embedding models (~1–2 GB); subsequent runs use the cached models from the mounted volume.
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In scheduled mode the process (and loaded models) stays resident between runs. The first run after a fresh install downloads InsightFace Buffalo_L (~300 MB); subsequent runs use the cached model from the mounted volume.
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---
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@@ -180,10 +169,8 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
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| Variable | Default | Description |
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| :--- | :--- | :--- |
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| `TRAINING_MODE` | `face` | `face` — upload crops to Frigate; `object` — save crops to disk |
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| `STRATEGY` | `adaptive` | `adaptive` — embedding-based diversity selection, stops when candidates become redundant; `standard` — fixed 30 images; `broad` — fixed 100 images |
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| `LIMIT` | *(unset)* | Exact image count — overrides `STRATEGY` |
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| `OBJECT_CLASS` | `dog` | Target class for object mode (any YOLO class: `dog`, `cat`, `car`, etc.) |
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| `AUTO_MODE` | *(auto)* | Skip interactive prompts and process all people unattended — auto-detected when no TTY is present (Docker, cron); set `true` to force in a terminal |
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| `VERBOSE` | `false` | Enable DEBUG-level console output (log file is always DEBUG) |
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@@ -227,7 +214,6 @@ These defaults are tuned for Frigate's ArcFace requirements. winnow will warn on
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| `OPENVINO_DEVICE` | `CPU` | Intel variant only: set `GPU` to use Arc or iGPU; default runs on CPU |
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| `ENABLE_CACHE` | `true` | Cache computed embeddings to disk (speeds up re-runs on the same library) |
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| `CACHE_DIR` | `.if_cache` | Path for embedding cache and upload tracker files |
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| `HF_HOME` | *(system)* | HuggingFace model cache path (SigLIP) |
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| `INSIGHTFACE_HOME` | *(system)* | InsightFace model cache path (Buffalo_L) |
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### Output
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