diff --git a/core/pathex.py b/core/pathex.py
index d0766cd..709b763 100644
--- a/core/pathex.py
+++ b/core/pathex.py
@@ -21,7 +21,7 @@ def scantree(path):
         else:
             yield entry
 
-def get_image_paths(dir_path, image_extensions=image_extensions, subdirs=False):
+def get_image_paths(dir_path, image_extensions=image_extensions, subdirs=False, return_Path_class=False):
     dir_path = Path (dir_path)
 
     result = []
@@ -34,7 +34,7 @@ def get_image_paths(dir_path, image_extensions=image_extensions, subdirs=False):
 
         for x in list(gen):
             if any([x.name.lower().endswith(ext) for ext in image_extensions]):
-                result.append(x.path)
+                result.append( x.path if not return_Path_class else Path(x.path) )
     return sorted(result)
 
 def get_image_unique_filestem_paths(dir_path, verbose_print_func=None):
diff --git a/models/Model_FANSeg/Model.py b/models/Model_FANSeg/Model.py
index dd2246d..d7645c9 100644
--- a/models/Model_FANSeg/Model.py
+++ b/models/Model_FANSeg/Model.py
@@ -25,7 +25,7 @@ class FANSegModel(ModelBase):
         if self.is_first_run() or ask_override:
             self.ask_autobackup_hour()
             self.ask_target_iter()
-            self.ask_batch_size(4)
+            self.ask_batch_size(24)
 
         #if self.is_first_run():
         #resolution = io.input_int("Resolution", default_resolution, add_info="64-512")
@@ -56,7 +56,7 @@ class FANSegModel(ModelBase):
         mask_shape = nn.get4Dshape(resolution,resolution,1)
  
         # Initializing model classes
-        self.model = TernausNet('FANSeg', 
+        self.model = TernausNet(f'{self.model_name}_FANSeg', 
                                  resolution, 
                                  FaceType.toString(self.face_type), 
                                  load_weights=not self.is_first_run(),
@@ -102,8 +102,7 @@ class FANSegModel(ModelBase):
                 loss = tf.reduce_mean(gpu_losses)
                 
                 loss_gv_op = self.model.opt.get_update_op (nn.tf_average_gv_list (gpu_loss_gvs))
-            
-            
+  
         
             # Initializing training and view functions
             def train(input_np, target_np):
@@ -126,7 +125,7 @@ class FANSegModel(ModelBase):
 
             src_generator = SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
                                                 sample_process_options=SampleProcessor.Options(random_flip=True),
-                                                output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,  'ct_mode':'idt', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                            'face_type':self.face_type, 'motion_blur':(25, 5),  'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
+                                                output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,  'ct_mode':'lct', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR,                                                            'face_type':self.face_type, 'motion_blur':(25, 5),  'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
                                                                         {'sample_type': SampleProcessor.SampleType.FACE_MASK,                    'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G,   'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type,                                                 'data_format':nn.data_format, 'resolution': resolution},
                                                                         ],
                                                 generators_count=src_generators_count )