diff --git a/convert_weights.py b/convert_weights.py
index 01f656e..a752ded 100644
--- a/convert_weights.py
+++ b/convert_weights.py
@@ -21,6 +21,8 @@
     'spp', False, 'Use SPP version of YOLOv3')
 tf.app.flags.DEFINE_string(
     'ckpt_file', './saved_model/model.ckpt', 'Chceckpoint file')
+tf.app.flags.DEFINE_integer(
+    'size', 416, 'Image size')
 
 
 def main(argv=None):
@@ -39,7 +41,8 @@ def main(argv=None):
 
     with tf.variable_scope('detector'):
         detections = model(inputs, len(classes),
-                           data_format=FLAGS.data_format)
+                           data_format=FLAGS.data_format,
+                           img_size=[FLAGS.size, FLAGS.size])
         load_ops = load_weights(tf.global_variables(
             scope='detector'), FLAGS.weights_file)
 
diff --git a/convert_weights_pb.py b/convert_weights_pb.py
index 1ade65a..9aa4e5b 100644
--- a/convert_weights_pb.py
+++ b/convert_weights_pb.py
@@ -23,6 +23,7 @@
     'tiny', False, 'Use tiny version of YOLOv3')
 tf.app.flags.DEFINE_bool(
     'spp', False, 'Use SPP version of YOLOv3')
+
 tf.app.flags.DEFINE_integer(
     'size', 416, 'Image size')
 
@@ -42,7 +43,7 @@ def main(argv=None):
     inputs = tf.placeholder(tf.float32, [None, FLAGS.size, FLAGS.size, 3], "inputs")
 
     with tf.variable_scope('detector'):
-        detections = model(inputs, len(classes), data_format=FLAGS.data_format)
+        detections = model(inputs, len(classes), data_format=FLAGS.data_format, img_size=[FLAGS.size, FLAGS.size])
         load_ops = load_weights(tf.global_variables(scope='detector'), FLAGS.weights_file)
 
     # Sets the output nodes in the current session
diff --git a/utils.py b/utils.py
index c4cce93..6e5bfc8 100644
--- a/utils.py
+++ b/utils.py
@@ -20,7 +20,8 @@ def get_boxes_and_inputs(model, num_classes, size, data_format):
 
     with tf.variable_scope('detector'):
         detections = model(inputs, num_classes,
-                           data_format=data_format)
+                           data_format=data_format,
+                           img_size=[size, size])
 
     boxes = detections_boxes(detections)
 
diff --git a/yolo_v3.py b/yolo_v3.py
index ef1d1a7..15992e5 100644
--- a/yolo_v3.py
+++ b/yolo_v3.py
@@ -200,7 +200,7 @@ def _upsample(inputs, out_shape, data_format='NCHW'):
     return inputs
 
 
-def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False, with_spp=False):
+def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False, with_spp=False, img_size=[416, 416]):
     """
     Creates YOLO v3 model.
 
@@ -213,8 +213,6 @@ def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=Fa
     :param with_spp: whether or not is using spp layer.
     :return:
     """
-    # it will be needed later on
-    img_size = inputs.get_shape().as_list()[1:3]
 
     # transpose the inputs to NCHW
     if data_format == 'NCHW':
@@ -277,7 +275,7 @@ def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=Fa
                 return detections
 
 
-def yolo_v3_spp(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False):
+def yolo_v3_spp(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False, img_size=[416, 416]):
     """
     Creates YOLO v3 with SPP  model.
 
@@ -289,4 +287,4 @@ def yolo_v3_spp(inputs, num_classes, is_training=False, data_format='NCHW', reus
     :param reuse: whether or not the network and its variables should be reused.
     :return:
     """
-    return yolo_v3(inputs, num_classes, is_training=is_training, data_format=data_format, reuse=reuse, with_spp=True)
+    return yolo_v3(inputs, num_classes, is_training=is_training, data_format=data_format, reuse=reuse, with_spp=True, img_size=img_size)
diff --git a/yolo_v3_tiny.py b/yolo_v3_tiny.py
index 5f308f5..4ea996e 100644
--- a/yolo_v3_tiny.py
+++ b/yolo_v3_tiny.py
@@ -15,7 +15,7 @@
             (81, 82),  (135, 169),  (344, 319)]
 
 
-def yolo_v3_tiny(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False):
+def yolo_v3_tiny(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False, img_size=[416, 416]):
     """
     Creates YOLO v3 tiny model.
 
@@ -27,8 +27,6 @@ def yolo_v3_tiny(inputs, num_classes, is_training=False, data_format='NCHW', reu
     :param reuse: whether or not the network and its variables should be reused.
     :return:
     """
-    # it will be needed later on
-    img_size = inputs.get_shape().as_list()[1:3]
 
     # transpose the inputs to NCHW
     if data_format == 'NCHW':