Pipeline complet Radiacode 103 - identification automatique d'isotopes

- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89%
- Generation 50k spectres synthetiques 1D (12-24h durees)
- Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell)
- Detection continue avec soustraction du background
- Capture background 24h avec gestion deconnexion
- Docker Compose : conteneur train (GPU) + detect (CPU/USB)
- Modele entraite inclus (vega_best.pt, 395 Mo)

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

docker-compose.yml

@@ -0,0 +1,55 @@
+services:
+  train:
+    build:
+      context: ./train
+      dockerfile: Dockerfile
+    volumes:
+      - ./data:/data
+      - ./models:/models
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    environment:
+      - NVIDIA_VISIBLE_DEVICES=1
+      - CUDA_VISIBLE_DEVICES=1
+      - DATA_DIR=/data/synthetic
+      - MODEL_DIR=/models
+      - NUM_SAMPLES=50000
+      - EPOCHS=100
+      - BATCH_SIZE=64
+      - LEARNING_RATE=0.001
+      - DETECTOR=radiacode_103
+      - MIN_DURATION=43200
+      - MAX_DURATION=86400
+      - SEED=42
+
+  detect:
+    build:
+      context: ./detect
+      dockerfile: Dockerfile
+    volumes:
+      - ./models:/models:ro
+      - ./logs:/logs
+      - ./data:/data
+    devices:
+      - /dev/bus/usb:/dev/bus/usb
+    privileged: true
+    environment:
+      - MODEL_PATH=/models/vega_best.pt
+      - ISOTOPE_INDEX_PATH=/models/vega_isotope_index.txt
+      - BACKGROUND_PATH=/data/background_24h.npy
+      - VEGA_ML_PATH=/models/vega_ml
+      - VEGA_DEVICE=cpu
+      - LOG_DIR=/logs
+      - SAMPLE_INTERVAL=60
+      - REPORT_HOUR=0
+      - MIN_LIVE_TIME=3600
+      - THRESHOLD=0.5
+    depends_on:
+      train:
+        condition: service_completed_successfully
+    restart: unless-stopped