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app.py

@@ -7,10 +7,8 @@ import numpy as np
 
 # Modèles optimisés pour le temps réel
 REALTIME_MODELS = {
-    "YOLOS Tiny (ultra-rapide)": "hustvl/yolos-tiny",
-    "YOLOS Small": "hustvl/yolos-small",
-    "DETR ResNet-50": "facebook/detr-resnet-50",
-    "Conditional DETR (optimisé)": "microsoft/conditional-detr-resnet-50"
+    "YOLOS (tiny-sized) model": "hustvl/yolos-tiny",
+    "RT-DETR": "PekingU/rtdetr_r18vd"
 }
 
 # Variables globales
@@ -28,6 +26,7 @@ def load_detector(model_name):
         current_detector = pipeline(
             "object-detection",
             model=model_id,
+            verbose=False,
             device=0 if torch.cuda.is_available() else -1
         )
         current_model_name = model_name
@@ -87,11 +86,6 @@ def process_webcam(image, model_choice, confidence_threshold):
         detections = detector(resized_image)
         print(f"🎯 Détections brutes: {len(detections)}")
         
-        # Debug: afficher le format des détections pour DETR
-        if detections and model_choice == "DETR ResNet-50":
-            print(f"🔧 Debug DETR - Première détection: {detections[0]}")
-            print(f"🔧 Keys disponibles: {list(detections[0].keys())}")
-        
         # Filtrer par confiance
         filtered_detections = [
             det for det in detections 
@@ -170,7 +164,7 @@ demo = gr.Interface(
         ),
         gr.Dropdown(
             choices=list(REALTIME_MODELS.keys()),
-            value="YOLOS Tiny (ultra-rapide)",
+            value="YOLOS (tiny-sized) model",
             label="Modèle"
         ),
         gr.Slider(0.1, 1.0, 0.1, step=0.1, label="Confiance")