Automatically classify and tag your wildlife photos with AI precision
RT-DETR object detection combined with iNat21 species classification identifies 10,000+ species automatically.
Process thousands of photos in minutes. 10x faster with GPU acceleration - up to 10 photos per second.
Works seamlessly with Lightroom Classic, On1 PhotoRAW, Immich, and other photo management tools.
Your photos never leave your computer. All AI processing happens locally - no cloud uploads, no data sharing, complete privacy.
Get started with a single Docker command. No complex installation, no dependencies to manage - just run and go.
Checkpoint-based processing lets you pause and resume anytime. Perfect for large collections - pick up right where you left off.
Continuously monitor folders for new photos. Perfect for tethered shooting or automatic imports from your camera.
Intelligent pairing of RAW and JPEG files. One shared XMP sidecar for both - works with NEF, CR2, CR3, ARW, DNG, and more.
# Create project directory
mkdir -p lumina-docker/{photos,cache} && cd lumina-docker
# Download docker-compose.yml
curl -o docker-compose.yml https://raw.githubusercontent.com/stevenvanassche/Lumina/master/docker/docker-compose.yml
# Create .env file
cat > .env << EOF
PHOTOS_PATH=./photos
CACHE_PATH=./cache
EOF
# Add your photos to photos/ directory, then run:
docker compose --profile cpu up # CPU mode (~1 photo/sec)
docker compose --profile gpu up # GPU mode (~10 photos/sec)
# NEW in v1.1: Watch mode - continuously monitor for new photos
LUMINA_WATCH_MODE=on docker compose --profile cpu up
First Run: Models download automatically (~2.5GB, 5-15 minutes)
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Automatically tag thousands of bird and animal species in your photo collection.
Organize your collection with AI precision. Works with your existing workflow.
Batch process biodiversity data for ecological studies and citizen science.
Start processing your photos with AI precision today