Add

app.py

@@ -24,43 +24,79 @@ _tf_model = None
 
 
 class CNN_Torch(nn.Module):
+    """
+    CNN amélioré 4 blocs
+    Entrée : (B, 3, 150, 150)
+    Sortie : (B, num_classes)
+    """
+
     def __init__(self, num_classes=6):
         super().__init__()
 
         self.features = nn.Sequential(
+            # Block 1: 150 -> 75
             nn.Conv2d(3, 32, kernel_size=3, padding=1),
             nn.BatchNorm2d(32),
             nn.ReLU(inplace=True),
+
+            nn.Conv2d(32, 32, kernel_size=3, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ReLU(inplace=True),
+
             nn.MaxPool2d(2),
 
+            # Block 2: 75 -> 37
             nn.Conv2d(32, 64, kernel_size=3, padding=1),
             nn.BatchNorm2d(64),
             nn.ReLU(inplace=True),
+
+            nn.Conv2d(64, 64, kernel_size=3, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+
             nn.MaxPool2d(2),
-            nn.Dropout2d(0.1),
+            nn.Dropout2d(0.10),
 
+            # Block 3: 37 -> 18
             nn.Conv2d(64, 128, kernel_size=3, padding=1),
             nn.BatchNorm2d(128),
             nn.ReLU(inplace=True),
+
+            nn.Conv2d(128, 128, kernel_size=3, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+
+            nn.MaxPool2d(2),
+            nn.Dropout2d(0.15),
+
+            # Block 4: 18 -> 9
+            nn.Conv2d(128, 256, kernel_size=3, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(inplace=True),
+
             nn.MaxPool2d(2),
-            nn.Dropout2d(0.2),
+            nn.Dropout2d(0.20),
         )
 
         self.gap = nn.AdaptiveAvgPool2d(1)
 
         self.classifier = nn.Sequential(
             nn.Flatten(),
-            nn.Linear(128, 256),
+            nn.Linear(256, 256),
             nn.ReLU(inplace=True),
-            nn.Dropout(0.5),
-            nn.Linear(256, num_classes),
+            nn.Dropout(0.3),
+            nn.Linear(256, num_classes)
         )
 
     def forward(self, x):
         x = self.features(x)
         x = self.gap(x)
         x = self.classifier(x)
-        return F.log_softmax(x, dim=1)
+        return x
 
 
 def load_pytorch():
@@ -88,6 +124,14 @@ def load_pytorch():
     return _pytorch_model
 
 
+def load_tensorflow():
+    global _tf_model
+    if _tf_model is None:
+        import tensorflow as tf
+        _tf_model = tf.keras.models.load_model("parfait_model.keras")
+    return _tf_model
+
+
 def read_input_image():
     if "image" in request.files and request.files["image"].filename:
         return Image.open(io.BytesIO(request.files["image"].read())).convert("RGB")