diff --git a/modules/util/augment.py b/modules/util/augment.py
index f88b93df49a92d1d03402416ebe28aca300be4c3..9747836f44d3c07e28b12b9b32d5db3e14111935 100644
--- a/modules/util/augment.py
+++ b/modules/util/augment.py
@@ -43,48 +43,6 @@ def augment_image(
                 lambda: flip_fn(data, label),
                 lambda: (data, label))
 
-            # Randomly setting brightness of image (~50%)
-            # def brightness_fn(data, label):
-            #     delta = tf.random.uniform(minval=0, maxval=brightness_delta, dtype=tf.float32, shape=[])
-            #     return (tf.image.adjust_brightness(data, delta=delta),
-            #             tf.image.adjust_brightness(label, delta=delta))
-            #
-            # data, label = tf.cond(
-            #     tf.less_equal(tf.random.uniform([]), 0.5),
-            #     lambda: brightness_fn(data, label),
-            #     lambda: (data, label))
-            #
-            # # Randomly setting constrast (~50%)
-            # def contrast_fn(data, label):
-            #     factor = tf.random.uniform(
-            #         minval=contrast_factor[0],
-            #         maxval=contrast_factor[1],
-            #         dtype=tf.float32, shape=[])
-            #     return (tf.image.adjust_contrast(data, factor),
-            #             tf.image.adjust_contrast(label, factor))
-            #
-            # if contrast_factor:
-            #     data, label = tf.cond(
-            #         tf.less_equal(tf.random.uniform([]), 0.5),
-            #         lambda: contrast_fn(data, label),
-            #         lambda: (data, label))
-            #
-            # # Randomly setting saturation(~50%)
-            # def saturation_fn(data, label):
-            #     factor = tf.random.uniform(
-            #         minval=saturation[0],
-            #         maxval=saturation[1],
-            #         dtype=tf.float32,
-            #         shape=[])
-            #     return (tf.image.adjust_saturation(data, factor),
-            #             tf.image.adjust_saturation(label, factor))
-            #
-            # if saturation:
-            #     data, label = tf.cond(
-            #         tf.less_equal(tf.random.uniform([]), 0.5),
-            #         lambda: saturation_fn(data, label),
-            #         lambda: (data, label))
-
             return data, label
 
         # Randomly returning unchanged data (~20%)