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>

train/vega_ml/inference/run_inference.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+"""
+Run Vega Inference
+
+Simple script to run inference with a trained Vega model.
+"""
+
+import sys
+import argparse
+from pathlib import Path
+
+# Add project root to path
+PROJECT_ROOT = Path(__file__).parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from inference.vega_inference import run_inference_demo, VegaInference
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Run inference with trained Vega model"
+    )
+    
+    parser.add_argument(
+        "--model", "-m",
+        type=str,
+        default="models/vega_best.pt",
+        help="Path to model checkpoint"
+    )
+    parser.add_argument(
+        "--data", "-d",
+        type=str,
+        default="O:/master_data_collection/isotopev2",
+        help="Path to data directory with spectra"
+    )
+    parser.add_argument(
+        "--threshold", "-t",
+        type=float,
+        default=0.5,
+        help="Detection threshold (0-1)"
+    )
+    parser.add_argument(
+        "--spectrum", "-s",
+        type=str,
+        default=None,
+        help="Path to a specific spectrum file to analyze"
+    )
+    
+    args = parser.parse_args()
+    
+    # Make paths absolute
+    model_path = Path(args.model)
+    if not model_path.is_absolute():
+        model_path = PROJECT_ROOT / model_path
+    
+    if args.spectrum:
+        # Single spectrum inference
+        spectrum_path = Path(args.spectrum)
+        if not spectrum_path.is_absolute():
+            spectrum_path = PROJECT_ROOT / spectrum_path
+        
+        print(f"\nLoading model from: {model_path}")
+        inference = VegaInference(str(model_path))
+        
+        print(f"\nAnalyzing spectrum: {spectrum_path}")
+        prediction = inference.predict_from_file(
+            spectrum_path, 
+            threshold=args.threshold
+        )
+        
+        print("\n" + "=" * 60)
+        print("PREDICTION RESULTS")
+        print("=" * 60)
+        print(prediction.summary())
+        print("=" * 60)
+        
+    else:
+        # Demo mode - analyze all spectra in data directory
+        data_path = Path(args.data)
+        if not data_path.is_absolute():
+            data_path = PROJECT_ROOT / data_path
+        
+        run_inference_demo(
+            str(model_path),
+            str(data_path),
+            threshold=args.threshold
+        )
+    
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())