docs: add wiki, .env.example, and fix stale if-curator references

- Add docs/setup.md, docs/troubleshooting.md, docs/faq.md as user-facing wiki
- Add .env.example with all env vars and inline comments
- Fix compose.override.yml: rename service if-curator → winnow, update volume paths
- Fix CHANGELOG.md: rename if-curator → winnow in release notes
- Link docs from README

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

.env.example

@@ -0,0 +1,45 @@
+# ── Required ──────────────────────────────────────────────────────────────────
+IMMICH_URL=http://192.168.1.10:2283
+API_KEY=your-immich-api-key
+FRIGATE_URL=http://192.168.1.10:5000
+
+# ── Mode & Strategy ───────────────────────────────────────────────────────────
+AUTO_MODE=true
+# TRAINING_MODE: face = upload to Frigate face recognition API
+#                object = save crops to output dir for manual Frigate placement
+TRAINING_MODE=face
+# STRATEGY: auto = objective diversity (recommended), standard = 30 imgs, broad = 100 imgs
+STRATEGY=auto
+# LIMIT=50                    # Custom image count; overrides STRATEGY preset
+# OBJECT_CLASS=dog            # Object label for object mode (e.g. dog, cat, car)
+
+# ── People Filtering ──────────────────────────────────────────────────────────
+# ONLY_PEOPLE=John,Jane       # Comma-separated; process only these people
+# SKIP_PEOPLE=Unknown         # Comma-separated; skip these people
+# MIN_FACE_COUNT=5            # Skip people with fewer than N assets in Immich
+# 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)
+# 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)
+# MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 80)
+
+# ── Caching & Models ──────────────────────────────────────────────────────────
+FORCE_CPU=false
+ENABLE_CACHE=true
+CACHE_DIR=/app/.if_cache
+HF_HOME=/models/huggingface
+INSIGHTFACE_HOME=/models
+
+# ── Tracker overrides (one-shot — remove after use) ───────────────────────────
+# DRY_RUN=true                # Preview selection without downloading/uploading
+# RETRY_REJECTED=true         # Re-attempt previously rejected images
+# RESET_PERSON=John           # Clear uploaded+rejected history for one person
+
+# ── Scheduling ────────────────────────────────────────────────────────────────
+# Cron expression (unset = run once and exit)
+CRON_SCHEDULE=0 3 * * 0     # Every Sunday at 3 AM

CHANGELOG.md

@@ -8,7 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [0.1.0] - 2026-03-03
 
 ### Added
-- Initial release of `if-curator` — Immich to Frigate training set curator
+- Initial release of `winnow` — Immich to Frigate training set curator
 - **Face recognition prep**: InsightFace (ArcFace/Buffalo_L) embeddings on face crops for Frigate face recognition training
 - **Object/state classification prep**: SigLIP (Vision Transformer) embeddings with YOLOv9c object detection
 - **Smart diversity selection**: K-Medoids clustering + Farthest Point Sampling (FPS) with hard-example weighting

README.md

@@ -1,5 +1,7 @@
 [![Publish Docker Image](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml) [![Release](https://github.com/sudolulo/winnow/actions/workflows/release.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/release.yml) [![Lint](https://github.com/sudolulo/winnow/actions/workflows/lint.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/lint.yml) [![Test](https://github.com/sudolulo/winnow/actions/workflows/test.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/test.yml)
 
+**Docs:** [Setup Guide](docs/setup.md) · [Troubleshooting](docs/troubleshooting.md) · [FAQ](docs/faq.md)
+
 # winnow
 
 `winnow` pulls photos of people and objects from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition and object classification models.

compose.override.yml

@@ -1,8 +1,8 @@
 services:
-  if-curator:
+  winnow:
     build: .
     network_mode: host
     volumes:
-      - /code/if-curator/models:/models
+      - /code/winnow/models:/models
-      - /code/if-curator/embeddings:/app/.if_cache
+      - /code/winnow/embeddings:/app/.if_cache
-      - /code/if-curator/output:/app/output
+      - /code/winnow/output:/app/frigate_train

docs/faq.md

@@ -0,0 +1,63 @@
+# FAQ
+
+## Does winnow modify my Immich library?
+
+No. winnow only reads from Immich (assets, people, face bounding boxes). It never writes back to Immich or deletes anything.
+
+---
+
+## How many images should I upload to Frigate?
+
+The `auto` strategy decides this for you — it keeps selecting until adding more images would be redundant. In practice this is usually 20–60 per person. You can cap it with `MAX_AUTO_IMAGES` (default 80).
+
+Quality and diversity matter far more than volume. 30 well-spread images outperform 200 from the same week.
+
+---
+
+## What's the difference between face mode and object mode?
+
+- **Face mode**: Extracts and aligns face crops, uploads them directly to Frigate's face training API. This is for teaching Frigate to recognize specific people.
+- **Object mode**: Runs YOLO detection on full images and saves crops of a target class (dog, cat, car, etc.) to disk. Frigate has no API for object training data, so you place them manually.
+
+---
+
+## Can I run it without Frigate?
+
+Yes — in object mode, `FRIGATE_URL` is not used and crops are saved to the output volume. In face mode you need Frigate to receive the uploads, but you can use `DRY_RUN=true` to preview selection without uploading.
+
+---
+
+## How does auto-diversity mode work?
+
+winnow computes a vector embedding for each candidate image (what the face/object actually looks like — angle, lighting, expression). It then clusters those embeddings and picks representatives that are maximally spread across the embedding space. It stops when the next-most-different image is already close to something already selected. See the README for the full pipeline.
+
+---
+
+## Does it support multiple people in one run?
+
+Yes. By default it processes every named person in your Immich library. Use `ONLY_PEOPLE` to whitelist specific names or `SKIP_PEOPLE` to exclude them.
+
+---
+
+## What GPU is needed?
+
+Any NVIDIA GPU with CUDA 12.x support. The models (InsightFace Buffalo_L + SigLIP) fit comfortably in 4 GB VRAM. CPU mode works but is significantly slower.
+
+ARM builds (linux/arm64) use CPU-only — CUDA is not available on ARM.
+
+---
+
+## Does it work on Unraid / Proxmox / bare Docker?
+
+Yes — the `compose.yml` uses standard Docker volume mounts. The TrueNAS paths in the example (`/mnt/<pool>/...`) are just an example; replace them with whatever paths suit your setup.
+
+---
+
+## How do I update winnow?
+
+```bash
+docker compose pull
+docker compose up -d
+```
+
+The `latest` tag on GHCR tracks the `main` branch. Pinning to a version tag (e.g. `ghcr.io/sudolulo/winnow:v0.2.0`) is recommended for stability.

docs/setup.md

@@ -0,0 +1,88 @@
+# Setup Guide
+
+## Prerequisites
+
+- [Immich](https://immich.app) v1.106+ with face recognition enabled and people tagged
+- [Frigate](https://frigate.video) v0.16+ (face mode only)
+- Docker with the NVIDIA container toolkit (optional but strongly recommended)
+
+---
+
+## 1. Get your Immich API key
+
+1. Open Immich → **Account Settings** → **API Keys**
+2. Click **New API Key**, give it a name (e.g. `winnow`), copy the key
+
+---
+
+## 2. Get your Frigate URL
+
+This is the base URL of your Frigate instance, e.g. `http://192.168.1.10:5000`. Only needed for face mode — omit it entirely if you're using object mode.
+
+---
+
+## 3. Deploy with Docker Compose
+
+Copy [`compose.yml`](../compose.yml) and [`.env.example`](../.env.example) to a directory on your host:
+
+```bash
+mkdir winnow && cd winnow
+curl -O https://raw.githubusercontent.com/sudolulo/winnow/main/compose.yml
+curl -O https://raw.githubusercontent.com/sudolulo/winnow/main/.env.example
+cp .env.example .env
+```
+
+Edit `.env` with your values:
+
+```bash
+IMMICH_URL=http://192.168.1.10:2283
+API_KEY=your-immich-api-key
+FRIGATE_URL=http://192.168.1.10:5000
+```
+
+Edit the volume paths in `compose.yml` to match your storage layout (replace `/mnt/<pool>` with your actual path).
+
+Start it:
+
+```bash
+docker compose up -d
+```
+
+Logs:
+
+```bash
+docker compose logs -f winnow
+```
+
+---
+
+## 4. First run
+
+On the first run, winnow downloads the embedding models (~1–2 GB) from HuggingFace and InsightFace. This happens once — subsequent runs use the cached models from your mounted volume and start immediately.
+
+---
+
+## 5. Scheduling
+
+Set `CRON_SCHEDULE` in your `.env` to keep winnow running on a schedule:
+
+```
+CRON_SCHEDULE=0 3 * * 0   # Every Sunday at 3 AM
+```
+
+Without `CRON_SCHEDULE`, the container runs once and exits.
+
+---
+
+## TrueNAS Scale
+
+The included `compose.yml` uses TrueNAS-style volume paths. Replace `<pool>` with your pool name:
+
+```yaml
+volumes:
+  - /mnt/tank/winnow/models:/models
+  - /mnt/tank/winnow/embeddings:/app/.if_cache
+  - /mnt/tank/winnow/output:/app/frigate_train
+```
+
+GPU passthrough on TrueNAS requires the NVIDIA app to be installed from the TrueNAS catalog and the `deploy.resources.reservations.devices` block in `compose.yml` (already included).

docs/troubleshooting.md

@@ -0,0 +1,77 @@
+# Troubleshooting
+
+## Container exits immediately
+
+Check logs:
+```bash
+docker compose logs winnow
+```
+
+Common causes:
+- **Missing required env var** — `IMMICH_URL` or `API_KEY` not set
+- **Cannot reach Immich** — check the URL and that Immich is running; use `http://` not `https://` unless you have TLS set up
+
+---
+
+## "No people found" / nothing processed
+
+- Make sure Immich has completed face recognition and you have named people in your library
+- `YEARS_FILTER` defaults to 10 years — increase it if your tagged photos are older
+- `MIN_FACE_COUNT` skips people with few photos — lower or remove it
+
+---
+
+## Frigate upload fails
+
+- Confirm `FRIGATE_URL` is reachable from inside the container: `docker exec winnow curl $FRIGATE_URL/api/stats`
+- Check Frigate v0.16+ — older versions don't have the face training API
+- Set `DRY_RUN=true` to verify selection without uploading
+
+---
+
+## Models fail to download
+
+winnow downloads InsightFace and HuggingFace (SigLIP) models on first run.
+
+- Ensure the container has internet access
+- Confirm the model volume is mounted and writable
+- If behind a proxy, set `HTTP_PROXY` / `HTTPS_PROXY` env vars
+
+---
+
+## Running on CPU (no GPU)
+
+Set `FORCE_CPU=true`. Everything works but embedding computation is slower — expect several minutes per person instead of seconds.
+
+If you have a GPU but it's not being used:
+- Confirm the NVIDIA container toolkit is installed: `docker run --rm --gpus all nvidia/cuda:12.9.2-base-ubuntu22.04 nvidia-smi`
+- Confirm the `deploy.resources.reservations.devices` block is present in `compose.yml`
+
+---
+
+## Same images uploaded every run
+
+The upload tracker is stored in `CACHE_DIR` (`/app/.if_cache` by default). If this volume isn't persisted between runs, the tracker resets and images are re-uploaded.
+
+Make sure `/app/.if_cache` is mounted to a persistent host path.
+
+---
+
+## Re-uploading a specific person
+
+To clear the upload history for one person and start fresh:
+
+```env
+RESET_PERSON=John
+```
+
+Remove this after one run — it clears the history and then processes normally.
+
+---
+
+## Image quality issues
+
+- **Too blurry**: Lower `BLUR_THRESHOLD` (default 100) — e.g. `50` accepts more blur
+- **Face too small**: Lower `MIN_FACE_WIDTH` (default 50px)
+- **Low confidence detections included**: Raise `MIN_CONFIDENCE` (default 0.7)
+- **Rejected images being re-tried**: Set `RETRY_REJECTED=true` for one run