diff --git a/facelib/XSegNet.py b/facelib/XSegNet.py
index 88acab2..f260e21 100644
--- a/facelib/XSegNet.py
+++ b/facelib/XSegNet.py
@@ -41,7 +41,6 @@ class XSegNet(object):
             self.model_weights = self.model.get_weights()
 
         model_name = f'{name}_{resolution}'
-
         self.model_filename_list = [ [self.model, f'{model_name}.npy'] ]
 
         if training:
@@ -59,6 +58,7 @@ class XSegNet(object):
                 return nn.tf_sess.run ( [pred], feed_dict={self.input_t :input_np})[0]
             self.net_run = net_run
 
+        self.initialized = True
         # Loading/initializing all models/optimizers weights
         for model, filename in self.model_filename_list:
             do_init = not load_weights
@@ -66,12 +66,16 @@ class XSegNet(object):
             if not do_init:
                 model_file_path = self.weights_file_root / filename
                 do_init = not model.load_weights( model_file_path )
-                if do_init and raise_on_no_model_files:
-                    raise Exception(f'{model_file_path} does not exists.')
+                if do_init:
+                    if raise_on_no_model_files:
+                        raise Exception(f'{model_file_path} does not exists.')
+                    if not training:
+                        self.initialized = False
+                        break
 
             if do_init:
                 model.init_weights()
-    
+        
     def get_resolution(self):
         return self.resolution
         
@@ -86,6 +90,9 @@ class XSegNet(object):
             model.save_weights( self.weights_file_root / filename )
 
     def extract (self, input_image):
+        if not self.initialized:
+            return 0.5*np.ones ( (self.resolution, self.resolution, 1), nn.floatx.as_numpy_dtype )
+            
         input_shape_len = len(input_image.shape)            
         if input_shape_len == 3:
             input_image = input_image[None,...]