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docs: move to GitHub wiki (https://github.com/sudolulo/winnow/wiki)

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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flan
2026-06-12 03:48:53 +00:00
co-authored by Claude Sonnet 4.6
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docs/faq.md
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# 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. Replace the example paths with whatever absolute 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
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# 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 point to directories on your host where models, cache, and output crops should be stored:
```yaml
volumes:
- /your/path/to/models:/models
- /your/path/to/cache:/app/.if_cache
- /your/path/to/output:/app/frigate_train
```
These directories will be created automatically by Docker if they don't exist.
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.
---
## GPU passthrough
To enable GPU acceleration, include the `deploy` block in `compose.yml` (already present in the example) and ensure the NVIDIA container toolkit is installed on your host:
```bash
# Verify GPU is accessible to Docker
docker run --rm --gpus all nvidia/cuda:12.9.2-base-ubuntu22.04 nvidia-smi
```
CPU mode works without any GPU setup — set `FORCE_CPU=true` to disable GPU explicitly.
docs/troubleshooting.md
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# 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