diff --git a/README.md b/README.md
index cae491d4..94d4e0ee 100644
--- a/README.md
+++ b/README.md
@@ -299,7 +299,7 @@ Video speed is adjusted approximately 50 times slower than actual speed.
   docker run --rm -it \
   -v `pwd`:/workdir \
   -w /workdir \
-  ghcr.io/pinto0309/onnx2tf:1.26.1
+  ghcr.io/pinto0309/onnx2tf:1.26.2
 
   or
 
@@ -307,7 +307,7 @@ Video speed is adjusted approximately 50 times slower than actual speed.
   docker run --rm -it \
   -v `pwd`:/workdir \
   -w /workdir \
-  docker.io/pinto0309/onnx2tf:1.26.1
+  docker.io/pinto0309/onnx2tf:1.26.2
 
   or
 
diff --git a/onnx2tf/__init__.py b/onnx2tf/__init__.py
index 6fbb5f6a..598f1226 100644
--- a/onnx2tf/__init__.py
+++ b/onnx2tf/__init__.py
@@ -1,3 +1,3 @@
 from onnx2tf.onnx2tf import convert, main
 
-__version__ = '1.26.1'
+__version__ = '1.26.2'
diff --git a/onnx2tf/onnx2tf.py b/onnx2tf/onnx2tf.py
index 5e9014e4..b58d570e 100644
--- a/onnx2tf/onnx2tf.py
+++ b/onnx2tf/onnx2tf.py
@@ -1626,6 +1626,7 @@ def sanitizing(node):
                             mean,
                             std,
                         ]
+
             elif custom_input_op_name_np_data_path is not None:
                 for param in custom_input_op_name_np_data_path:
                     if len(param) != 4:
@@ -1652,11 +1653,14 @@ def sanitizing(node):
 
             # representative_dataset_gen
             def representative_dataset_gen():
-                for idx in range(data_count):
+                batch_size = model.inputs[0].shape[0]
+                if not isinstance(batch_size, int):
+                    batch_size = 1
+                for idx in range(0, data_count, batch_size):
                     yield_data_dict = {}
                     for model_input_name in model_input_name_list:
                         calib_data, mean, std = calib_data_dict[model_input_name]
-                        normalized_calib_data: np.ndarray = (calib_data[idx] - mean) / std
+                        normalized_calib_data: np.ndarray = (calib_data[idx:idx+batch_size] - mean) / std
                         yield_data_dict[model_input_name] = tf.cast(tf.convert_to_tensor(normalized_calib_data), tf.float32)
                     yield yield_data_dict
 
@@ -1708,7 +1712,7 @@ def representative_dataset_gen():
                     inf_type_input = tf.float32
                 else:
                     inf_type_input = tf.int8
-                
+
                 if output_quant_dtype == 'int8':
                     inf_type_output = tf.int8
                 elif output_quant_dtype == 'uint8':