AI Powered Photo Gallery Without the Cloud

Arkalogy released Best Photo Picker (bpp), a free, open-source, local-first photo curation tool that auto-scores images for sharpness, lighting, faces, and composition without uploading to the cloud. The tool runs entirely on the user's machine, clusters near-duplicates, and allows boosting specific people, addressing privacy concerns and subscription fatigue associated with Apple Photos and Google Photos.

Drowning in 15,000 family photos? Apple Photos and Google Photos bury your best shots in their feed. Best Photo Picker runs entirely on your machine, scores every photo on sharpness, lighting, faces, and composition, and lets you curate the perfect 50 in minutes — no cloud, no subscription, no compromise. It's the only photo tool that auto-scores your whole library for curation without uploading anything , then clusters near-duplicates and lets you boost specific people so the final pick favors the faces that matter — your kid, for the grandparents. Try it in 30 seconds — no photos needed: pip install "bppicker web " && bpp demo Generates a sample library, runs the full UI locally, and quits cleanly when you close the tab. Nothing leaves your machine. By Arkalogy — a PM-directed, AI-built product; the architectural decisions are captured in docs/adr/ . Why & how it was built →For an annotated walkthrough of the full workflow — import → adjust → pick → export — plus the side surfaces faces, calendar, map, duplicates , see docs/quickstart-gallery.md . 📣 Found a bug or have a feature idea? Open an issue https://github.com/Arkalogy/best-photo-picker/issues/new/choose · Start a discussion https://github.com/Arkalogy/best-photo-picker/discussions · Contributing guide https://github.com/Arkalogy/best-photo-picker/blob/main/CONTRIBUTING.md . Security issues: please use private vulnerability reporting https://github.com/Arkalogy/best-photo-picker/security/advisories/new , not a public issue. | Best Photo Picker | Apple Photos | Google Photos | Lightroom | digiKam | | |---|---|---|---|---|---| | Local-first / runs offline | ✅ | ❌ cloud | ✅ | || | Auto-scores photos for picking | ✅ | ❌ | ❌ | || | Boost picks by named person | ✅ | ❌ | ❌ | || | Near-duplicate deduplication | ✅ | ❌ | ✅ | || | Free + open source | ✅ MIT | ❌ | ❌ | ❌ $120/yr | ✅ GPL | | No account / no subscription | ✅ | ❌ | ❌ | ❌ | ✅ | | Optional LAN sharing w/ device pairing | ✅ | ❌ | ❌ | ❌ | ❌ | If you live in the Apple/Google ecosystem and your collection fits their feeds, those tools work great. bpp exists for the case they're bad at: you have thousands of photos of a specific subject — your kid, your dog, last summer's trip — and you want to find the actual best fifty without uploading anything anywhere. Quality scoring — sharpness, exposure, face detection YuNet + SCRFD + BlazeFace + MediaPipe + dlib , composition Smart deduplication — perceptual hashing dHash + aHash and CLIP semantic similarity Temporal diversity — per-day caps and monthly coverage for balanced selection Face recognition — automatic clustering, per-person smart albums, merge, dismiss, reassign, drag-to-fix mis-detected bboxes Library management — import by copying to managed library with SHA-256 dedup Album system — manual + 19 smart album types person, time, score, duplicates, pets, etc. Interactive web UI — real-time slider tuning, photo grid, lightbox, compare view, batch operations Desktop app — native macOS app via Tauri v2 wraps the web UI in a native window Soft delete — 30-day recovery, Recently Deleted album Demo mode — try instantly with generated sample photos Privacy — local-first, no telemetry, no analytics. The few network calls bpp makes model downloads on first analyze, OpenStreetMap tiles when you open the Map view, update checks against GitHub Releases, optional LAN sharing, optional pip install of extra features are all enumerated below and individually disclosable; nothing about your library is ever sent. Requires Python 3.11 3.12+ is not yet supported — it's pinned to match the desktop sidecar . On macOS, Homebrew ships a newer Python by default; install 3.11 with brew install python@3.11 or , or use the no-Python pyenv desktop app below. Recommended — pipx https://pipx.pypa.io/ keeps bpp in its own environment and makes updating one command: pipx install "bppicker web " Update to the latest release later: pipx upgrade bppicker Plain pip works too: pip install "bppicker web " Optional: face recognition pip install "bppicker web,faces " Optional: HEIC support pip install "bppicker heic " Most ML-powered features face recognition, NudeNet, RAW import, HEIC, AI inpainting install on demand from Settings → Advanced → ML Models in the running app, or you can pre-install everything at once with pip install "bppicker heic,faces,raw,nudity,inpaint " . Prefer a dock icon over a terminal? Each release ships a standalone Mac app. No Python, no terminal: - Download always the latest release . BestPhotoPicker-macOS-arm64.dmg https://github.com/Arkalogy/best-photo-picker/releases/latest/download/BestPhotoPicker-macOS-arm64.dmg - Double-click the .dmg and drag Best Photo Picker into Applications . - Open it from Applications or Spotlight. The app is signed with an Apple Developer ID and notarized by Apple, so it opens on double-click — no security warning. To update, download the newer .dmg the same way; your photo library and settings live outside the app and carry over untouched. Apple Silicon M1/M2/M3 and later only. On an Intel Mac, or on Windows/Linux, use the pipx install "bppicker web " path above. bpp demo Generates sample photos and launches the web UI — no configuration needed. Start the photo management server default library: ~/Pictures/BestPhotoPicker bpp serve Or specify a custom library path bpp serve --library ~/Photos/2024 Open http://127.0.0.1:5001 http://127.0.0.1:5001 in your browser. Import folders, adjust scoring weights with sliders, and export your curated selection. One-shot: analyze + select best 50 photos bpp run --input ~/Photos/MyKid --k 50 --out ~/curated --gallery Or step-by-step: bpp analyze --input ~/Photos/MyKid --out ~/workdir bpp select --workdir ~/workdir --k 50 --out ~/curated --gallery Import → Analyze → Score → Deduplicate → Select → Export Import : photos copied to managed library with SHA-256 dedup Analyze : parallel feature extraction blur, exposure, faces, composition cached in SQLite Score : weighted combination of sub-scores, tunable in real-time via sliders Deduplicate : perceptual hash clustering removes burst duplicates; optional CLIP semantic dedup Select : greedy selection with per-day caps and monthly coverage for temporal diversity Export : copy/hardlink/symlink selected photos with optional HTML gallery | Feature | Description | |---|---| | Photo grid | Thumbnail grid with score badges, zoom control, sort & filter | | Lightbox | Full-size viewer with keyboard navigation | | Sliders | Real-time weight tuning blur, exposure, face, composition | | BPP Picks | Sidebar sub-item picks best K photos across the full library; toolbar chip filters picks in the current album/view | | Albums | Manual and smart albums by time, score, person | | Faces | Auto-clustered face detection with merge, dismiss, and per-person albums | | Batch ops | Multi-select with Cmd/Ctrl-click, bulk include/exclude/favorite | | Import | Drag folders or archives into the library | | Export | Copy or link selected photos to an output directory | | Parameter | Default | Description | |---|---|---| blur weight | 0.30 | Weight for sharpness in aggregate score | exposure weight | 0.20 | Weight for exposure quality | face weight | 0.35 | Weight for face detection & framing | composition weight | 0.15 | Weight for composition rule of thirds | max long side | 1024 | Downscale images to this before analysis | hash distance threshold | 10 | dHash Hamming distance for "same" image | time window seconds | 15 | Cluster burst photos within this window | max per day | 3 | Max selected photos per calendar day | min per month | 1 | Try to include at least 1 per month | bpp runs all ML inference on CPU by default. ONNX-based models SCRFD face detection, CLIP semantic search, YOLOv11n pet detection can opt into hardware acceleration through the BPP ONNX PROVIDERS env var: Apple Silicon M1/M2/M3 — use the Apple Neural Engine via CoreML BPP ONNX PROVIDERS="CoreMLExecutionProvider,CPUExecutionProvider" \ bpp serve --library ~/Pictures/BestPhotoPicker NVIDIA GPU Linux — install onnxruntime-gpu first pip uninstall -y onnxruntime && pip install onnxruntime-gpu BPP ONNX PROVIDERS="CUDAExecutionProvider,CPUExecutionProvider" \ bpp serve --library ~/Pictures/BestPhotoPicker Windows DirectML BPP ONNX PROVIDERS="DmlExecutionProvider,CPUExecutionProvider" \ bpp serve --library ~/Pictures/BestPhotoPicker CPU is always appended as the final fallback so a missing provider in your wheel falls through cleanly with a warning rather than crashing. Ordering matters — providers are tried in the order listed. | Surface | Tested in CI | Supported | Notes | |---|---|---|---| macOS arm64 M1/M2/M3 — CPU | yes dev box | yes | Primary dev target. Tauri desktop app ships only this. | Linux x86 64 — CPU | yes ubuntu-latest | yes | Default for PyPI install. | Linux arm64 — CPU | no | yes | Docker image builds; runtime not exercised in CI. | Windows — CPU | no | yes | Code path exists; never run end-to-end. Reports welcome via GitHub issues. | macOS arm64 — CoreMLExecutionProvider ANE | no | yes | Code path exists. Expected 5-10× face inference speedup; correctness depends on the specific ONNX graph and ONNX Runtime version. | Linux x86 64 — CUDAExecutionProvider | no | yes | Requires pip install onnxruntime-gpu replaces base onnxruntime . Free-tier CI doesn't have a GPU runner. | Windows — DmlExecutionProvider | no | yes | Same caveat as Windows CPU — code path exists, no end-to-end run. | "Supported" means the code path accepts the configuration and shouldn't crash; report bugs via GitHub Issues if it does. "Tested in CI" means we run the full test suite on every push for that surface. Anything between the two is on the user's risk surface — your hardware, your driver stack, your call. We'd love to expand the tested column as users report working setups; PRs welcome that add matrix entries to .github/workflows/ci.yml . Face extraction is single-threaded per subprocess by default face extract workers=1 . On a 6,000-photo library that's ~50 min on Apple Silicon roughly 0.5 s/photo . Two config knobs let operators trade RAM for time: settings table or YAML config face extract workers: 4 number of parallel workers face extract pool: process "process" | "thread" Recommended combos: | Setup | workers | pool | Expected throughput | Peak RAM | |---|---|---|---|---| | Default safe everywhere | 1 | thread | 1× baseline ~50 min / 6k photos | ~700 MB | | Power user, 16+ GB RAM | 4 | process | ~3-4× ~13 min / 6k photos | ~3 GB | Measured on Apple M-series; results vary by photo resolution and system load. The process pool is the only memory-safe parallel option — there is an audited race in the embed/landmark thread-stack that can SIGSEGV a multi-threaded worker. ProcessPool gives each worker its own model arena so any race is isolated. Trade-off: each worker pays a ~5-10 s cold start for model loads, so it's only worthwhile for libraries with hundreds of photos or more. The native-thread-pool pinning OMP NUM THREADS / OPENBLAS NUM THREADS / MKL NUM THREADS = 1 at bpp/web/face worker.py and bpp/web/analyze worker.py module-import time applies regardless of pool choice — required to prevent nested-thread oversubscription that would crash the child silently. See tests/test face thread safety.py for the full regression guard suite. Open the library on a phone, tablet, or another computer connected to the same Wi-Fi. Two-step flow: phone scans QR, owner approves on Mac. After that, the device is trusted and reconnects silently. - Open Settings → Share on the host machine - Toggle LAN sharing on. First time only : if the server started with sharing off, it bound to loopback only and the toggle replies "Restart required" . Quit and relaunch bpp once — the next start picks up the persisted flag and binds the LAN interface. Subsequent toggles flip instantly. - The pane fills in with a share URL, a scannable QR code, and a live Devices list - On phone: point the camera at the QR code → tap the link - Phone shows a "Waiting for the owner to approve…" page - On Mac: a new card appears under Pending requests e.g. "iPhone — 192.168.1.42" within a few seconds - Click Approve → phone auto-reloads into the full app - The phone moves into Trusted devices and stays there. Future visits skip the approval step entirely. Trusted devices → Revoke kicks the device immediately. The phone shows "Access revoked" within ~3 seconds. Pending requests → Block rejects a request you didn't make e.g. someone else scanned the QR by accident .- The phone never silently re-requests access. To ask again, the phone user has to actively tap "Request access again" on the revoked page. If the URL leaked entirely someone screenshotted it, you sent it to the wrong person : Settings → Share → Revoke rotates the share token. All current URLs stop working immediately. A new URL + QR replaces the old one. Already-trusted devices keep working until you also revoke them individually. Pending requests with badge count when phones are waiting Trusted devices with last-seen timestamps and a "previously revoked" tag if you'd kicked them before Recent access — last 10 share-link auth events deduped per 10-minute window Only enable on networks you trust — home Wi-Fi, not coffee shops or hotel networks. This is LAN-only: there is no cloud relay and no internet exposure. For the threat model, schema, and OSS extension hooks HTTPS, ProxyFix, future user accounts , see docs/security.md https://github.com/Arkalogy/best-photo-picker/blob/main/docs/security.md . --host defaults to 127.0.0.1 loopback only when LAN sharing is off, and 0.0.0.0 when sharing is on at startup. The canonical way to enable LAN access is Settings → Share , not the flag — pass --host explicitly only for reverse-proxy / Docker setups. A double-clickable launcher template ships at launch.command.template ; copy to launch.command , mark executable chmod +x , and Finder-double-click to start the server + Tauri window with the documented default library path. Override via BPP LIBRARY env var. A Dockerfile ships at the repo root for self-hosters who want to run bpp behind a reverse proxy. It's a multi-stage build that installs the web,faces,nudity,heic extras; the README's "native macOS app" framing applies to the Tauri desktop wrapper, not the server. Bind to loopback inside the container and publish via -p 127.0.0.1:5001:5001 — the LAN-sharing toggle is owner- only by design and isn't appropriate for an internet-exposed container. Set BPP TRUSTED PROXIES to your reverse proxy's CIDR so the LAN gate honors X-Forwarded-For correctly; see docs/security.md https://github.com/Arkalogy/best-photo-picker/blob/main/docs/security.md . bpp serve --library PATH --host HOST --port 5001 --no-browser --config FILE bpp demo --port 5001 --no-browser --keep bpp web --input DIR --workdir DIR --port 5001 --no-browser bpp analyze --input DIR --out WORKDIR --config FILE --max N --workers K bpp select --workdir DIR --k N --out DIR --copy|--hardlink|--symlink --gallery bpp run --input DIR --k N --out DIR all options from analyze + select bpp pick LIBRARY --top N --boost-face NAME ... --out DIR --json|--paths-only --quality original|high|medium|low --dry-run bpp model {list|accept|accepted|use-context|byom|remove|registry} — text-mode parity with the GUI click-through for restricted-license models. Use when running headless, in CI, or scripting the acceptance flow. bpp db restore-backup --library PATH --previous --yes --accept-stale --force Global: --seed INT default 42 , --extensions STR default "jpg,jpeg,png,heic" This tool is local-first — your photos never leave your machine for analysis, scoring, or selection. There is no telemetry and no cloud account. Network usage is limited to the following — each is independently disclosable, and the ones that fire automatically update check, map tiles, model downloads on first analyze can be turned off: Model downloads first-run only — face detection, CLIP, etc., fetched from public model hosts: HuggingFace huggingface.co , Google Storage storage.googleapis.com , the OpenCV opencv zoo on media.githubusercontent.com , OpenAI's Azure CDN for the CLIP tokenizer vocab openaipublic.azureedge.net , GitHub releases for Ultralytics/YOLO github.com/ultralytics , and GitHub releases for LaMa inpainting weights github.com/enesmsahin , only when bppicker inpaint is installed and the user clicks "Remove object" Runtime dependency installs only when you click Install in Settings → Advanced → ML Models — runs pip install against PyPI to fetch optional packages like face recognition, NudeNet, ONNX Runtime, or AI inpainting. TLS-only trust; transitive dependencies are not hash-pinned same trust level as pip install from a terminal . Model weights fetched at first use are all SHA-256 pinned and verified by bpp before loading, including the LaMa inpainting weights big-lama.pt . Update check background, default-on — calls api.github.com to see if a new version is out; can be turned off in Settings → App. Sends only a generic User-Agent and your current version — no library data, no telemetry. 256 KB response cap enforced in bpp client-side — oversized responses are refused before any bytes are parsed Map tiles only when viewing photo locations on the Map view or in the lightbox for a geo-tagged photo — OpenStreetMap is queried with the tile coordinates around your photo. Each visited tile reveals approximate photo coordinates to OSM's tile-server access logs. The map view is opt-in by user action you have to open it ; no tile fetches happen on first paint LAN sharing only if you enable it — phone access over your local Wi-Fi; never traverses the internet All photo analysis runs on your machine. Originals are never sent anywhere, and soft-delete keeps recoverable copies for 30 days. Python 3.11 CI tests this version only — 3.12+ is not validated . On macOS, Homebrew ships 3.14 by default — install 3.11 via brew install python@3.11 or pyenv install 3.11 . Node.js 18+ and npm for desktop app only Rust stable, for desktop app only — install via rustup.rs https://rustup.rs CMake + C++ compiler for dlib face recognition — macOS: brew install cmake git clone https://github.com/Arkalogy/best-photo-picker.git cd best-photo-picker python3.11 -m venv .venv source .venv/bin/activate pip install -e ". dev,web,faces " Optional: HEIC support pip install -e ". heic " bpp serve --library ~/Pictures/BestPhotoPicker --no-browser Open http://127.0.0.1:5001 The desktop app wraps the web UI in a native macOS window via Tauri. Install JS dependencies cd desktop && npm install && cd .. Dev mode starts Python server + Tauri window cd desktop && npm run dev For a standalone .app bundle launchable from Finder/Spotlight : Build sidecar PyInstaller-bundled Python server cd desktop && ./build-sidecar.sh Build .app + .dmg npm run build Output: desktop/src-tauri/target/release/bundle/ The sidecar binary lands at desktop/src-tauri/binaries/bpp-server-{target-triple} e.g., bpp-server-aarch64-apple-darwin . Python ruff check . && ruff format --check . pytest -v full pytest suite Frontend JS — requires npm install once npm run lint ESLint + auto-generated globals allowlist npm run format:check Prettier scoped to dev-tool JS npm run typecheck tsc --noEmit on @ts-check files npm run test:js Vitest + jsdom unit tests under tests-js/ End-to-end smoke tests Playwright npx playwright install chromium one-time browser install start bpp serve first, then: npm run test:e2e full Playwright suite under tests-e2e/ bpp/ cli.py, commands.py CLI entry points scoring/ Blur, exposure, face SCRFD/BlazeFace/dlib , composition dedupe/ Perceptual hash + CLIP semantic dedup selection/ Greedy chooser with diversity constraints db/ SQLite schema, migrations, CRUD web/ Flask app, blueprint-per-feature, background workers static/js/ Vanilla JS SPA modules under static/js/modules/ templates/index.html Single-page HTML shell desktop/ src-tauri/ Tauri v2 Rust app build-sidecar.sh PyInstaller sidecar build scripts/dev-macos.sh Dev launcher — integration branch. Feature branches merge here. develop — stable/release. Only main develop → main merges.- CI runs on PRs to main only lint + pytest, Python 3.11 . cd desktop ./build-sidecar.sh builds the Python server into a standalone binary npm run build builds .app + .dmg The .dmg is at desktop/src-tauri/target/release/bundle/dmg/ . Open it and drag to /Applications . Note:Without an Apple Developer certificate, macOS Gatekeeper will block the app. To open it: right-click → Open → Open. This only needs to be done once. The library folder default ~/Pictures/BestPhotoPicker/ contains everything: BestPhotoPicker/ photos/ your imported photos organized by batch data/ SQLite database scores, faces, albums, settings cache/ thumbnails and face crops regenerated automatically logs/ server logs To move to a new machine: - Copy the entire BestPhotoPicker/ folder to the new machine same path or any location - Install the app on the new machine - Start it: bpp serve --library ~/Pictures/BestPhotoPicker The app automatically detects that file paths have changed different username, different OS path and remaps all photos by SHA-256 hash on first startup. Your scores, albums, face clusters, favorites — everything is preserved. The cache/ folder can be skipped to save transfer time; thumbnails regenerate on demand. dlib fails to install — Requires CMake and a C++ compiler. macOS: brew install cmake . Ubuntu: sudo apt install cmake build-essential . HEIC photos not recognized — pip install "bppicker heic " . Linux may also need libheif-dev . Port 5001 in use — bpp serve --port 8080 , or check what's holding it: lsof -i :5001 . ImportError: cv2-related errors after upgrading or installing extras — bpp declares opencv-python-headless as its base OpenCV. bppicker faces mediapipe pulls in opencv-contrib-python and bppicker inpaint simple-lama-inpainting pulls in opencv-python . All three install into the same cv2 namespace and the last-installed one wins on disk. With matching version numbers it's harmless, but a future minor bump in any of them can produce hard-to-diagnose import-order errors. If you hit one, reset to the headless variant: pip uninstall opencv-python opencv-contrib-python && pip install --force-reinstall opencv-python-headless . Desktop app won't reopen from dock — Quit fully Cmd+Q , then relaunch. The Tauri app handles dock-click reopen. App fails to start after an upgrade / failed migration — every schema migration writes a verified data/photopicker.db.backup before mutating anything. To roll back the live DB to that snapshot: Stop any running server first. pkill -f 'bpp serve' Restore. The current DB is moved aside with a timestamped suffix so nothing is destroyed. Use --previous to roll back to the older .backup.prev snapshot if .backup is also bad. bpp db restore-backup --library ~/Pictures/BestPhotoPicker The command verifies the backup's integrity before swapping it in and refuses if the backup is corrupt. Once the app starts and your library looks correct, you can delete the moved-aside files manually. Downgrading bpp to an older version — bpp's schema migrations are forward-only . Once a newer bpp version migrates your library DB, an older bpp will refuse to open it or crash on missing columns . To roll back: - Install the older bpp version pip install bppicker==X.Y.Z or reinstall the matching bundle . - Restore the pre-migration snapshot: pkill -f 'bpp serve' bpp db restore-backup --library ~/Pictures/BestPhotoPicker --previous - Start the older bpp. The --previous flag restores .backup.prev — the snapshot taken before the most recent backup rotation, which is what bpp wrote just before the upgrade migration ran. This works for single- version rollbacks upgraded once, want to go back . If you've already started the new version multiple times or run multi-step migrations, .backup.prev may have been overwritten — at that point the only path back is restoring from your own external backup of the library directory. User-facing release notes live in CHANGELOG.md https://github.com/Arkalogy/best-photo-picker/blob/main/CHANGELOG.md . If you find this useful, star the repo https://github.com/Arkalogy/best-photo-picker and spread the word. Bug reports and PRs welcome. ☕ Buy us a coffee https://buymeacoffee.com/arkalogy — covers a model-download CDN bill and a focused afternoon of bug fixes 📬 Hire Arkalogy mailto:support@arkalogy.com — custom integrations, AI-assisted product work, or PM consulting Built and maintained by Arkalogy https://arkalogy.com . bpp started as a tool to triage a large family photo library without sending it to anyone's cloud — most existing options either live in someone else's data center or are designed for professional photographers. If you have the same problem and this helps, that's the win. This codebase was built with Claude Anthropic — implementation and line-by-line code review are both AI-driven, not human. The maintainer works as the product owner and architect: directing the work, approving every commit, and capturing load-bearing architectural decisions in docs/adr/ /Arkalogy/best-photo-picker/blob/main/docs/adr . It's a deliberate experiment in how far a PM-led, AI-built product can go while staying disciplined — hence the registries, the test gates, the legal posture in , and the ADRs. /Arkalogy/best-photo-picker/blob/main/MODEL POLICY.md MODEL POLICY.md MIT — see LICENSE https://github.com/Arkalogy/best-photo-picker/blob/main/LICENSE . See NOTICE.txt https://github.com/Arkalogy/best-photo-picker/blob/main/NOTICE.txt for the full third-party license breakdown. bpp itself and every dependency installed by default are permissively licensed MIT / Apache-2.0 / BSD / HPND . Copyleft components are strictly opt-in and never installed without an explicit extra: pulls in bppicker heic pillow-heif GPLv2 binary wheel, due to bundled x265; Python source is BSD-3 for HEIC support.pulls in bppicker nudity NudeNet GPL-3.0 for the optional NSFW filter. If you distribute a derived work that bundles NudeNet, GPL-3.0 terms attach to the whole work — that's why we ship it as an opt-in extra and never as a default dependency.pulls in bppicker raw rawpy MIT, links to LibRaw which is LGPL-2.1 for camera-RAW support.pulls in bppicker inpaint simple-lama-inpainting Apache 2.0 for AI object removal, plus torch and torchvision both BSD-3 transitively. The LaMa model weights are downloaded on first use from the upstream LaMa research project advimman/lama https://github.com/advimman/lama and have their own license terms — review them before redistributing a derived work. The Ultralytics YOLOv11n weights downloaded for pet detection are AGPL-3.0; we use the ONNX-exported weights only no Ultralytics Python code runs in your process . See NOTICE.txt /Arkalogy/best-photo-picker/blob/main/NOTICE.txt for the full picture. Bppicker bundles no model weights. Every optional model is downloaded by your local installation directly from the upstream provider on first use, subject to upstream model terms — bppicker does not redistribute or relicense them. Arkalogy will not monetize, sell, or market Best Photo Picker for commercial workflows. Because the source code is MIT-licensed, third parties may still use it commercially; restricted-model access is separately controlled by model-specific terms and app-level gates commercial-use gate, hard-block, click-through acknowledgment . Optional third-party models may have separate licenses, including non-commercial or research-only restrictions — commercial users must select commercial-safe models or provide their own licensed weights. For face recognition specifically, bppicker's default embedder SFace is permissively licensed and commercial-use-safe. Other face embedders are available behind an opt-in click-through; some are research-only / non-commercial and are hard-blocked when you select "Commercial" as your use context. The Bring-Your-Own-Model path lets you point bppicker at a local ONNX file you have your own rights to.