diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py
index ae182dc..c632941 100644
--- a/src/eynollah/eynollah.py
+++ b/src/eynollah/eynollah.py
@@ -309,6 +309,7 @@ class Eynollah:
 
         return img_new, img_is_resized
 
+    # FIXME: actually may run enhancement model, should be renamed
     def resize_image_with_column_classifier(self, image):
         self.logger.debug("enter resize_image_with_column_classifier")
         img = self.imread(image, binary=self.input_binary)
@@ -316,19 +317,36 @@ class Eynollah:
         width_early = img.shape[1]
         page_img, page_coord = self.early_page_for_num_of_column_classification(img)
 
-        if self.input_binary:
-            img_in = page_img
-        else:
-            img_1ch = self.imread(image, grayscale=True, uint8=False)
-            img_1ch = img_1ch[page_coord[0]: page_coord[1],
-                              page_coord[2]: page_coord[3]]
-            img_in = np.repeat(img_1ch[:, :, np.newaxis], 3, axis=2)
-        img_in = img_in / 255.0
-        img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST).astype(np.float16)
+        label_p_pred = np.ones(6)
+        conf_col = 1.0
+        if self.num_col_upper and not self.num_col_lower:
+            num_col = self.num_col_upper
+        elif self.num_col_lower and not self.num_col_upper:
+            num_col = self.num_col_lower
+        elif (not self.num_col_upper and not self.num_col_lower or
+              self.num_col_upper != self.num_col_lower):
+            if self.input_binary:
+                img_in = page_img
+            else:
+                img_1ch = self.imread(image, grayscale=True)
+                img_1ch = img_1ch[page_coord[0]: page_coord[1],
+                                  page_coord[2]: page_coord[3]]
+                img_in = np.repeat(img_1ch[:, :, np.newaxis], 3, axis=2)
+            img_in = img_in / 255.0
+            img_in = cv2.resize(img_in, (448, 448), interpolation=cv2.INTER_NEAREST).astype(np.float16)
 
-        label_p_pred = self.model_zoo.get("col_classifier").predict(img_in[np.newaxis], verbose=0)[0]
-        num_col = np.argmax(label_p_pred) + 1
-        conf_col = np.max(label_p_pred)
+            label_p_pred = self.model_zoo.get("col_classifier").predict(img_in[np.newaxis], verbose=0)[0]
+            num_col = np.argmax(label_p_pred) + 1
+            conf_col = np.max(label_p_pred)
+            if self.num_col_upper and self.num_col_upper < num_col:
+                num_col = self.num_col_upper
+                conf_col = 1.0
+            if self.num_col_lower and self.num_col_lower > num_col:
+                num_col = self.num_col_lower
+                conf_col = 1.0
+        else:
+            num_col = self.num_col_upper
+            conf_col = 1.0
 
         self.logger.info("Found %s columns (%s)", num_col, np.around(label_p_pred, decimals=5))
         if num_col in (1, 2):
@@ -349,6 +367,7 @@ class Eynollah:
         image['scale_x'] = 1.0 * img_new.shape[1] / img.shape[1]
         return
 
+    # FIXME: does not actually run enhancement model, should be renamed
     def resize_and_enhance_image_with_column_classifier(self, image):
         self.logger.debug("enter resize_and_enhance_image_with_column_classifier")
         dpi = image['dpi']