flanandClaude Sonnet 4.6
b7dc87b05b
- 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>
64 lines
2.5 KiB
Markdown
64 lines
2.5 KiB
Markdown
# FAQ
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## Does winnow modify my Immich library?
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No. winnow only reads from Immich (assets, people, face bounding boxes). It never writes back to Immich or deletes anything.
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---
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## How many images should I upload to Frigate?
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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).
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Quality and diversity matter far more than volume. 30 well-spread images outperform 200 from the same week.
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---
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## What's the difference between face mode and object mode?
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- **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.
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- **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.
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---
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## Can I run it without Frigate?
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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.
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---
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## How does auto-diversity mode work?
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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.
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---
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## Does it support multiple people in one run?
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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.
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---
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## What GPU is needed?
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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.
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ARM builds (linux/arm64) use CPU-only — CUDA is not available on ARM.
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---
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## Does it work on Unraid / Proxmox / bare Docker?
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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.
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---
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## How do I update winnow?
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```bash
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docker compose pull
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docker compose up -d
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```
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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.
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