diff --git a/src/eynollah/sbb_binarize.py b/src/eynollah/sbb_binarize.py
index 851ac7d..37ac7c3 100644
--- a/src/eynollah/sbb_binarize.py
+++ b/src/eynollah/sbb_binarize.py
@@ -19,7 +19,7 @@ from eynollah.model_zoo import EynollahModelZoo
 tf_disable_interactive_logs()
 import tensorflow as tf
 from tensorflow.python.keras import backend as tensorflow_backend
-
+from pathlib import Path 
 from .utils import is_image_filename
 
 def resize_image(img_in, input_height, input_width):
@@ -347,7 +347,7 @@ class SbbBinarizer:
             self.logger.info("Found %d image files to binarize in %s", len(ls_imgs), dir_in)
             for i, image_path in enumerate(ls_imgs):
                 self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_path)
-                image_stem = image_path.split('.')[0]
+                image_stem = Path(image_path).stem
                 image = cv2.imread(os.path.join(dir_in,image_path) )
                 img_last = 0
                 model_file, model = self.models
diff --git a/src/eynollah/training/cli.py b/src/eynollah/training/cli.py
index 65a7a8a..3718275 100644
--- a/src/eynollah/training/cli.py
+++ b/src/eynollah/training/cli.py
@@ -9,6 +9,7 @@ from .generate_gt_for_training import main as generate_gt_cli
 from .inference import main as inference_cli
 from .train import ex
 from .extract_line_gt import linegt_cli
+from .weights_ensembling import main as ensemble_cli
 
 @click.command(context_settings=dict(
         ignore_unknown_options=True,
@@ -26,3 +27,4 @@ main.add_command(generate_gt_cli, 'generate-gt')
 main.add_command(inference_cli, 'inference')
 main.add_command(train_cli, 'train')
 main.add_command(linegt_cli, 'export_textline_images_and_text')
+main.add_command(ensemble_cli, 'ensembling')
diff --git a/src/eynollah/training/generate_gt_for_training.py b/src/eynollah/training/generate_gt_for_training.py
index 693cab8..30abd04 100644
--- a/src/eynollah/training/generate_gt_for_training.py
+++ b/src/eynollah/training/generate_gt_for_training.py
@@ -474,7 +474,7 @@ def visualize_layout_segmentation(xml_file, dir_xml, dir_out, dir_imgs):
         img_file_name_with_format = find_format_of_given_filename_in_dir(dir_imgs, f_name)
         img = cv2.imread(os.path.join(dir_imgs, img_file_name_with_format))
             
-        co_text, co_graphic, co_sep, co_img, co_table, co_noise, y_len, x_len = get_layout_contours_for_visualization(xml_file)
+        co_text, co_graphic, co_sep, co_img, co_table, co_map, co_noise, y_len, x_len = get_layout_contours_for_visualization(xml_file)
         
         
         added_image = visualize_image_from_contours_layout(co_text['paragraph'], co_text['header']+co_text['heading'], co_text['drop-capital'], co_sep, co_img, co_text['marginalia'], co_table, img)
diff --git a/src/eynollah/training/gt_gen_utils.py b/src/eynollah/training/gt_gen_utils.py
index 28ab422..50bb6fa 100644
--- a/src/eynollah/training/gt_gen_utils.py
+++ b/src/eynollah/training/gt_gen_utils.py
@@ -15,7 +15,7 @@ with warnings.catch_warnings():
     warnings.simplefilter("ignore")
     
     
-def visualize_image_from_contours_layout(co_par, co_header, co_drop, co_sep, co_image, co_marginal, co_table, img):
+def visualize_image_from_contours_layout(co_par, co_header, co_drop, co_sep, co_image, co_marginal, co_table, co_map, img):
     alpha = 0.5
     
     blank_image = np.ones( (img.shape[:]), dtype=np.uint8) * 255
@@ -28,6 +28,7 @@ def visualize_image_from_contours_layout(co_par, co_header, co_drop, co_sep, co_
     col_sep = (255, 0, 0)
     col_marginal =  (106, 90, 205)
     col_table =  (0, 90, 205)
+    col_map =  (90, 90, 205)
     
     if len(co_image)>0:
         cv2.drawContours(blank_image, co_image, -1, col_image, thickness=cv2.FILLED)  # Fill the contour
@@ -52,6 +53,9 @@ def visualize_image_from_contours_layout(co_par, co_header, co_drop, co_sep, co_
         
     if len(co_table)>0:
         cv2.drawContours(blank_image, co_table, -1, col_table, thickness=cv2.FILLED)  # Fill the contour
+        
+    if len(co_map)>0:
+        cv2.drawContours(blank_image, co_map, -1, col_map, thickness=cv2.FILLED)  # Fill the contour
     
     img_final =cv2.cvtColor(blank_image, cv2.COLOR_BGR2RGB)
     
@@ -231,7 +235,12 @@ def update_region_contours(co_text, img_boundary, erosion_rate, dilation_rate, y
         con_eroded = return_contours_of_interested_region(img_boundary_in,pixel, min_size )
         
         try:
-            co_text_eroded.append(con_eroded[0])
+            if len(con_eroded)>1:
+                cnt_size = np.array([cv2.contourArea(con_eroded[j]) for j in range(len(con_eroded))])
+                cnt = contours[np.argmax(cnt_size)]
+                co_text_eroded.append(cnt)
+            else:
+                co_text_eroded.append(con_eroded[0])
         except:
             co_text_eroded.append(con)
         
@@ -377,6 +386,7 @@ def get_layout_contours_for_visualization(xml_file):
     co_sep=[]
     co_img=[]
     co_table=[]
+    co_map=[]
     co_noise=[]
     
     types_text = []
@@ -593,6 +603,31 @@ def get_layout_contours_for_visualization(xml_file):
                     elif vv.tag!=link+'Point' and sumi>=1:
                         break
                 co_table.append(np.array(c_t_in))
+                
+        if tag.endswith('}MapRegion') or tag.endswith('}mapregion'):
+            #print('sth')
+            for nn in root1.iter(tag):
+                c_t_in=[]
+                sumi=0
+                for vv in nn.iter():
+                    # check the format of coords
+                    if vv.tag==link+'Coords':
+                        coords=bool(vv.attrib)
+                        if coords:
+                            p_h=vv.attrib['points'].split(' ')
+                            c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ]  for x in p_h] ) )
+                            break
+                        else:
+                            pass
+    
+    
+                    if vv.tag==link+'Point':
+                        c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
+                        sumi+=1
+                    #print(vv.tag,'in')
+                    elif vv.tag!=link+'Point' and sumi>=1:
+                        break
+                co_map.append(np.array(c_t_in))
     
 
         if tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'):
@@ -619,7 +654,7 @@ def get_layout_contours_for_visualization(xml_file):
                     elif vv.tag!=link+'Point' and sumi>=1:
                         break
                 co_noise.append(np.array(c_t_in))
-    return co_text, co_graphic, co_sep, co_img, co_table, co_noise, y_len, x_len
+    return co_text, co_graphic, co_sep, co_img, co_table, co_map, co_noise, y_len, x_len
     
 def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_file, config_params, printspace, dir_images, dir_out_images):
     """
@@ -698,12 +733,15 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
             _, thresh = cv2.threshold(imgray, 0, 255, 0)
 
             contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
+            
             cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
-
-            cnt = contours[np.argmax(cnt_size)]
-
-            x, y, w, h = cv2.boundingRect(cnt)
+            
+            try:
+                cnt = contours[np.argmax(cnt_size)]
+                x, y, w, h = cv2.boundingRect(cnt)
+            except:
+                x, y , w, h = 0, 0, x_len, y_len
+            
             bb_xywh = [x, y, w, h]
             
             
@@ -835,7 +873,7 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
                 types_graphic_label = list(types_graphic_dict.values())
 
                 
-            labels_rgb_color = [ (0,0,0), (255,0,0), (255,125,0), (255,0,125), (125,255,125), (125,125,0), (0,125,255), (0,125,0), (125,125,125), (255,0,255), (125,0,125), (0,255,0),(0,0,255), (0,255,255), (255,125,125),  (0,125,125), (0,255,125), (255,125,255), (125,255,0)]
+            labels_rgb_color = [ (0,0,0), (255,0,0), (255,125,0), (255,0,125), (125,255,125), (125,125,0), (0,125,255), (0,125,0), (125,125,125), (255,0,255), (125,0,125), (0,255,0),(0,0,255), (0,255,255), (255,125,125),  (0,125,125), (0,255,125), (255,125,255), (125,255,0), (125,255,255)]
             
             
             region_tags=np.unique([x for x in alltags if x.endswith('Region')])   
@@ -846,6 +884,7 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
             co_sep=[]
             co_img=[]
             co_table=[]
+            co_map=[]
             co_noise=[]
             
             for tag in region_tags:
@@ -1056,6 +1095,32 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
                                 elif vv.tag!=link+'Point' and sumi>=1:
                                     break
                             co_table.append(np.array(c_t_in))
+                            
+                if 'mapregion' in keys:
+                    if tag.endswith('}MapRegion') or tag.endswith('}mapregion'):
+                        #print('sth')
+                        for nn in root1.iter(tag):
+                            c_t_in=[]
+                            sumi=0
+                            for vv in nn.iter():
+                                # check the format of coords
+                                if vv.tag==link+'Coords':
+                                    coords=bool(vv.attrib)
+                                    if coords:
+                                        p_h=vv.attrib['points'].split(' ')
+                                        c_t_in.append( np.array( [ [ int(x.split(',')[0]) , int(x.split(',')[1]) ]  for x in p_h] ) )
+                                        break
+                                    else:
+                                        pass
+                
+                
+                                if vv.tag==link+'Point':
+                                    c_t_in.append([ int(float(vv.attrib['x'])) , int(float(vv.attrib['y'])) ])
+                                    sumi+=1
+                                #print(vv.tag,'in')
+                                elif vv.tag!=link+'Point' and sumi>=1:
+                                    break
+                            co_map.append(np.array(c_t_in))
             
                 if 'noiseregion' in keys:
                     if tag.endswith('}NoiseRegion') or tag.endswith('}noiseregion'):
@@ -1129,6 +1194,10 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
                     erosion_rate = 0#2
                     dilation_rate = 3#4
                     co_table, img_boundary = update_region_contours(co_table, img_boundary, erosion_rate, dilation_rate, y_len, x_len )
+                if "mapregion" in elements_with_artificial_class:
+                    erosion_rate = 0#2
+                    dilation_rate = 3#4
+                    co_map, img_boundary = update_region_contours(co_map, img_boundary, erosion_rate, dilation_rate, y_len, x_len )
                     
                     
                 
@@ -1154,6 +1223,8 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
                     img_poly=cv2.fillPoly(img, pts =co_img, color=labels_rgb_color[ config_params['imageregion']])
                 if 'tableregion' in keys:  
                     img_poly=cv2.fillPoly(img, pts =co_table, color=labels_rgb_color[ config_params['tableregion']])
+                if 'mapregion' in keys:  
+                    img_poly=cv2.fillPoly(img, pts =co_map, color=labels_rgb_color[ config_params['mapregion']])
                 if 'noiseregion' in keys:  
                     img_poly=cv2.fillPoly(img, pts =co_noise, color=labels_rgb_color[ config_params['noiseregion']])
                     
@@ -1214,6 +1285,9 @@ def get_images_of_ground_truth(gt_list, dir_in, output_dir, output_type, config_
                 if 'tableregion' in keys:
                     color_label = config_params['tableregion']
                     img_poly=cv2.fillPoly(img, pts =co_table, color=(color_label,color_label,color_label))
+                if 'mapregion' in keys:
+                    color_label = config_params['mapregion']
+                    img_poly=cv2.fillPoly(img, pts =co_map, color=(color_label,color_label,color_label))
                 if 'noiseregion' in keys:
                     color_label = config_params['noiseregion']
                     img_poly=cv2.fillPoly(img, pts =co_noise, color=(color_label,color_label,color_label))
diff --git a/src/eynollah/training/inference.py b/src/eynollah/training/inference.py
index 10fca6c..f74e9e1 100644
--- a/src/eynollah/training/inference.py
+++ b/src/eynollah/training/inference.py
@@ -26,6 +26,9 @@ from .models import (
     Patches
 )
 
+from.utils import (scale_padd_image_for_ocr)
+from eynollah.utils.utils_ocr import (decode_batch_predictions)
+
 with warnings.catch_warnings():
     warnings.simplefilter("ignore")
     
@@ -35,8 +38,7 @@ Tool to load model and predict for given image.
 """
 
 class sbb_predict:
-
-    def __init__(self,image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area):
+    def __init__(self,image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, cpu, out, min_area):
         self.image=image
         self.dir_in=dir_in
         self.patches=patches
@@ -48,6 +50,7 @@ class sbb_predict:
         self.config_params_model=config_params_model
         self.xml_file = xml_file
         self.out = out
+        self.cpu = cpu
         if min_area:
             self.min_area = float(min_area)
         else:
@@ -159,21 +162,19 @@ class sbb_predict:
             return mIoU
             
     def start_new_session_and_model(self):
-        
-        config = tf.compat.v1.ConfigProto()
-        config.gpu_options.allow_growth = True
+        if self.task == "cnn-rnn-ocr":
+            if self.cpu:
+                os.environ['CUDA_VISIBLE_DEVICES']='-1'
+            self.model = load_model(self.model_dir)
+            self.model = tf.keras.models.Model(
+                            self.model.get_layer(name = "image").input, 
+                            self.model.get_layer(name = "dense2").output)
+        else:
+            config = tf.compat.v1.ConfigProto()
+            config.gpu_options.allow_growth = True
 
-        session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
-        tensorflow_backend.set_session(session)
-        #tensorflow.keras.layers.custom_layer = PatchEncoder
-        #tensorflow.keras.layers.custom_layer = Patches
-        self.model = load_model(self.model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
-        #config = tf.ConfigProto()
-        #config.gpu_options.allow_growth=True
-    
-        #self.session = tf.InteractiveSession()
-        #keras.losses.custom_loss = self.weighted_categorical_crossentropy
-        #self.model = load_model(self.model_dir , compile=False)
+            session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
+            tensorflow_backend.set_session(session)
 
         
         ##if self.weights_dir!=None:
@@ -250,6 +251,30 @@ class sbb_predict:
             index_class = np.argmax(label_p_pred[0])
             
             print("Predicted Class: {}".format(classes_names[str(int(index_class))]))
+        elif self.task == "cnn-rnn-ocr":
+            img=cv2.imread(image_dir)
+            img = scale_padd_image_for_ocr(img, self.config_params_model['input_height'], self.config_params_model['input_width'])
+            
+            img = img / 255.
+            
+            with open(os.path.join(self.model_dir, "characters_org.txt"), 'r') as char_txt_f:
+                characters = json.load(char_txt_f)
+                
+            AUTOTUNE = tf.data.AUTOTUNE
+
+            # Mapping characters to integers.
+            char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
+            
+            # Mapping integers back to original characters.
+            num_to_char = StringLookup(
+                vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
+            )
+            preds = self.model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
+            pred_texts = decode_batch_predictions(preds, num_to_char)
+            pred_texts = pred_texts[0].replace("[UNK]", "")
+            return pred_texts
+            
+            
         elif self.task == 'reading_order':
             img_height = self.config_params_model['input_height']
             img_width = self.config_params_model['input_width']
@@ -580,6 +605,8 @@ class sbb_predict:
             elif self.task == 'enhancement':
                 if self.save:
                     cv2.imwrite(self.save,res)
+            elif self.task == "cnn-rnn-ocr":
+                print(f"Detected text: {res}")
             else:
                 img_seg_overlayed, only_layout  = self.visualize_model_output(res, self.img_org, self.task)
                 if self.save:
@@ -587,9 +614,9 @@ class sbb_predict:
                 if self.save_layout:
                     cv2.imwrite(self.save_layout, only_layout)
                     
-            if self.ground_truth:
-                gt_img=cv2.imread(self.ground_truth)
-                self.IoU(gt_img[:,:,0],res[:,:,0])
+                if self.ground_truth:
+                    gt_img=cv2.imread(self.ground_truth)
+                    self.IoU(gt_img[:,:,0],res[:,:,0])
             
         else:
             ls_images = os.listdir(self.dir_in)
@@ -603,6 +630,8 @@ class sbb_predict:
                 elif self.task == 'enhancement':
                     self.save = os.path.join(self.out, f_name+'.png')
                     cv2.imwrite(self.save,res)
+                elif self.task == "cnn-rnn-ocr":
+                    print(f"Detected text for file name {f_name} is: {res}")
                 else:
                     img_seg_overlayed, only_layout  = self.visualize_model_output(res, self.img_org, self.task)
                     self.save = os.path.join(self.out, f_name+'_overlayed.png')
@@ -610,9 +639,9 @@ class sbb_predict:
                     self.save_layout = os.path.join(self.out, f_name+'_layout.png')
                     cv2.imwrite(self.save_layout, only_layout)
                         
-                if self.ground_truth:
-                    gt_img=cv2.imread(self.ground_truth)
-                    self.IoU(gt_img[:,:,0],res[:,:,0])
+                    if self.ground_truth:
+                        gt_img=cv2.imread(self.ground_truth)
+                        self.IoU(gt_img[:,:,0],res[:,:,0])
             
 
         
@@ -668,24 +697,29 @@ class sbb_predict:
     "-xml",
     help="xml file with layout coordinates that reading order detection will be implemented on. The result will be written in the same xml file.",
 )
-
+@click.option(
+    "--cpu",
+    "-cpu",
+    help="For OCR, the default device is the GPU. If this parameter is set to true, inference will be performed on the CPU",
+    is_flag=True,
+)
 @click.option(
     "--min_area",
     "-min",
     help="min area size of regions considered for reading order detection. The default value is zero and means that all text regions are considered for reading order.",
 )
-def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_file, out, min_area):
+def main(image, dir_in, model, patches, save, save_layout, ground_truth, xml_file, cpu, out, min_area):
     assert image or dir_in, "Either a single image -i or a dir_in -di is required"
     with open(os.path.join(model,'config.json')) as f:
         config_params_model = json.load(f)
     task = config_params_model['task']
-    if task != 'classification' and task != 'reading_order':
+    if task != 'classification' and task != 'reading_order' and task != "cnn-rnn-ocr":
         if image and not save:
             print("Error: You used one of segmentation or binarization task with image input but not set -s, you need a filename to save visualized output with -s")
             sys.exit(1)
         if dir_in and not out:
             print("Error: You used one of segmentation or binarization task with dir_in but not set -out")
             sys.exit(1)
-    x=sbb_predict(image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area)
+    x=sbb_predict(image, dir_in, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, cpu, out, min_area)
     x.run()
 
diff --git a/src/eynollah/training/utils.py b/src/eynollah/training/utils.py
index 0a92935..005810f 100644
--- a/src/eynollah/training/utils.py
+++ b/src/eynollah/training/utils.py
@@ -1,7 +1,7 @@
 import os
 import math
 import random
-
+from pathlib import Path
 import cv2
 import numpy as np
 import seaborn as sns
@@ -32,6 +32,9 @@ def scale_padd_image_for_ocr(img, height, width):
     else:
         width_new = width
 
+    if width_new <= 0:
+        width_new = width
+
     img_res= resize_image (img, height, width_new)
     img_fin = np.ones((height, width, 3))*255
 
@@ -1335,7 +1338,8 @@ def data_gen_ocr(
     # TODO: Why while True + yield, why not return a list?
     while True:
         for i in ls_files_images:
-            f_name = i.split('.')[0]
+            print(i, 'i')
+            f_name = Path(i).stem#.split('.')[0]
 
             txt_inp  = open(os.path.join(dir_train, "labels/"+f_name+'.txt'),'r').read().split('\n')[0]
             
diff --git a/src/eynollah/training/weights_ensembling.py b/src/eynollah/training/weights_ensembling.py
new file mode 100644
index 0000000..6dce7fd
--- /dev/null
+++ b/src/eynollah/training/weights_ensembling.py
@@ -0,0 +1,136 @@
+import sys
+from glob import glob
+from os import environ, devnull
+from os.path import join
+from warnings import catch_warnings, simplefilter
+import os
+
+import numpy as np
+from PIL import Image
+import cv2
+environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+stderr = sys.stderr
+sys.stderr = open(devnull, 'w')
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.python.keras import backend as tensorflow_backend
+sys.stderr = stderr
+from tensorflow.keras import layers
+import tensorflow.keras.losses
+from tensorflow.keras.layers import *
+import click
+import logging
+
+
+class Patches(layers.Layer):
+    def __init__(self, patch_size_x, patch_size_y):
+        super(Patches, self).__init__()
+        self.patch_size_x = patch_size_x
+        self.patch_size_y = patch_size_y
+
+    def call(self, images):
+        #print(tf.shape(images)[1],'images')
+        #print(self.patch_size,'self.patch_size')
+        batch_size = tf.shape(images)[0]
+        patches = tf.image.extract_patches(
+            images=images,
+            sizes=[1, self.patch_size_y, self.patch_size_x, 1],
+            strides=[1, self.patch_size_y, self.patch_size_x, 1],
+            rates=[1, 1, 1, 1],
+            padding="VALID",
+        )
+        #patch_dims = patches.shape[-1]
+        patch_dims = tf.shape(patches)[-1]
+        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
+        return patches
+    def get_config(self):
+
+        config = super().get_config().copy()
+        config.update({
+            'patch_size_x': self.patch_size_x,
+            'patch_size_y': self.patch_size_y,
+        })
+        return config
+
+
+
+class PatchEncoder(layers.Layer):
+    def __init__(self, **kwargs):
+        super(PatchEncoder, self).__init__()
+        self.num_patches = num_patches
+        self.projection = layers.Dense(units=projection_dim)
+        self.position_embedding = layers.Embedding(
+            input_dim=num_patches, output_dim=projection_dim
+        )
+
+    def call(self, patch):
+        positions = tf.range(start=0, limit=self.num_patches, delta=1)
+        encoded = self.projection(patch) + self.position_embedding(positions)
+        return encoded
+    def get_config(self):
+
+        config = super().get_config().copy()
+        config.update({
+            'num_patches': self.num_patches,
+            'projection': self.projection,
+            'position_embedding': self.position_embedding,
+        })
+        return config
+    
+    
+def start_new_session():
+    ###config = tf.compat.v1.ConfigProto()
+    ###config.gpu_options.allow_growth = True
+
+    ###self.session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
+    ###tensorflow_backend.set_session(self.session)
+    
+    config = tf.compat.v1.ConfigProto()
+    config.gpu_options.allow_growth = True
+
+    session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
+    tensorflow_backend.set_session(session)
+    return session
+
+def run_ensembling(dir_models, out):
+    ls_models = os.listdir(dir_models)
+
+
+    weights=[]
+
+    for model_name in ls_models:
+        model =  load_model(os.path.join(dir_models,model_name) , compile=False, custom_objects={'PatchEncoder':PatchEncoder, 'Patches': Patches})
+        weights.append(model.get_weights())
+        
+    new_weights = list()
+
+    for weights_list_tuple in zip(*weights):
+        new_weights.append(
+            [np.array(weights_).mean(axis=0)\
+                for weights_ in zip(*weights_list_tuple)])
+        
+
+
+    new_weights = [np.array(x) for x in new_weights]
+        
+    model.set_weights(new_weights)
+    model.save(out)
+    os.system('cp '+os.path.join(os.path.join(dir_models,model_name) , "config.json ")+out)
+
+@click.command()
+@click.option(
+    "--dir_models",
+    "-dm",
+    help="directory of models",
+    type=click.Path(exists=True, file_okay=False),
+)
+@click.option(
+    "--out",
+    "-o",
+    help="output directory where ensembled model will be written.",
+    type=click.Path(exists=False, file_okay=False),
+)
+
+def main(dir_models, out):
+    run_ensembling(dir_models, out)
+