diff --git a/vr180_streaming/sam2_streaming_simple.py b/vr180_streaming/sam2_streaming_simple.py
index a8402e4..203832f 100644
--- a/vr180_streaming/sam2_streaming_simple.py
+++ b/vr180_streaming/sam2_streaming_simple.py
@@ -28,14 +28,23 @@ class SAM2StreamingProcessor:
         self.device = torch.device(config.get('hardware', {}).get('device', 'cuda'))
         
         # SAM2 model configuration
-        model_cfg = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
+        model_cfg_name = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
         checkpoint = config.get('matting', {}).get('sam2_checkpoint', 
                                                    'segment-anything-2/checkpoints/sam2.1_hiera_large.pt')
         
+        # Map config name to Hydra path (like the examples show)
+        config_mapping = {
+            'sam2.1_hiera_t': 'configs/sam2.1/sam2.1_hiera_t.yaml',
+            'sam2.1_hiera_s': 'configs/sam2.1/sam2.1_hiera_s.yaml',
+            'sam2.1_hiera_b+': 'configs/sam2.1/sam2.1_hiera_b+.yaml',
+            'sam2.1_hiera_l': 'configs/sam2.1/sam2.1_hiera_l.yaml',
+        }
+        
+        model_cfg = config_mapping.get(model_cfg_name, model_cfg_name)
+        
         # Build predictor (simple, clean approach)
-        # Note: SAM2 uses Hydra to find configs automatically in sam2/configs/
         self.predictor = build_sam2_video_predictor(
-            model_cfg,  # Just the config name, Hydra finds it automatically
+            model_cfg,  # Relative path from sam2 package
             checkpoint,
             device=self.device,
             vos_optimized=True  # Enable VOS optimizations for speed