diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..d6e795b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+
+Pipfile.lock
+
+yolov8x.pt
+
+.vscode/
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 0000000..bf26d6d
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,24 @@
+cmake_minimum_required(VERSION 3.0.2)
+project(road_segmentation)
+
+find_package(
+  catkin REQUIRED
+  COMPONENTS rospy
+             std_msgs
+             sensor_msgs
+             cv_bridge
+             jsk_recognition_msgs
+)
+
+catkin_python_setup()
+
+catkin_package(
+  CATKIN_DEPENDS
+    rospy
+    std_msgs
+    sensor_msgs
+    cv_bridge
+    jsk_recognition_msgs
+)
+
+include_directories(${catkin_INCLUDE_DIRS})
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+   6. Trademarks. This License does not grant permission to use the trade
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+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/Pipfile b/Pipfile
new file mode 100644
index 0000000..e473071
--- /dev/null
+++ b/Pipfile
@@ -0,0 +1,30 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[[source]]
+name = "pytorch_cuda"
+url = "https://download.pytorch.org/whl/cu118"
+verify_ssl = false
+
+[dev-packages]
+
+[packages]
+networkx = "==2.8.8"
+torch = {index = "pytorch_cuda",version = "==2.0.0+cu118"}
+torchvision = {index = "pytorch_cuda",version = "==0.15.1+cu118"}
+tensorflow = "==2.4.0"
+opencv-python = "*"
+numpy = "*"
+pandas = "*"
+matplotlib = "*"
+keras = "*"
+requests = "*"
+pyyaml = "*"
+rospkg = "*"
+ipython = "*"
+scikit-learn = "*"
+
+[requires]
+python_version = "3.8"
diff --git a/README.md b/README.md
index 89109d6..8a900ea 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,21 @@
 * ### Development environment
 
 
-  ubuntu 18.04, tensorflow 2.0.0, opencv-python 4.2.0.32, numpy 1.18.2
+  ubuntu 20.04, tensorflow 2.4.0, opencv-python 4.2.0.32, numpy 1.18.2
+
+* ### Setup
+- Install cuda 11.8
+- Install cuDNN 8.9.7 for cuda 11.8
+- add below in ~/.bashrc
+```bash
+## CUDA and cuDNN paths
+export PATH=/usr/local/cuda-11.8/bin:${PATH}
+export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:${LD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=/usr/local/cuda-11.8/targets/x86_64-linux/lib:${LD_LIBRARY_PATH}
+```
+- run `sudo ln -s /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcusolver.so.11 /usr/local/cuda-11.8/targets/x86_64-linux/lib/libcusolver.so.10`
+  - Reference:[WSL2 + Ubuntu20.04 + CUDA 11.4 で TensorFlow 環境構築](https://zenn.dev/ylabo0717/articles/48796b7f3470c7)
+- run `pipenv install` in this package
 
 * ### model
 
diff --git a/config/class.yaml b/config/class.yaml
new file mode 100644
index 0000000..960ae85
--- /dev/null
+++ b/config/class.yaml
@@ -0,0 +1,8 @@
+class:
+  Background: 0
+  Bike_lane: 1
+  Caution_zone: 2
+  Crosswalk: 3
+  braille_guide_blocks: 4
+  Roadway: 5
+  Sidewalk: 6
diff --git a/data_loader/split_train_test.py b/data_loader/split_train_test.py
deleted file mode 100644
index 0915a37..0000000
--- a/data_loader/split_train_test.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from data_loader import train_images, mask_images
-import shutil
-
-train_images = train_images()
-mask_images = mask_images()
-
-train_num = int(len(train_images)*0.75)
-
-test_img = train_images[train_num:]
-test_label = mask_images[train_num:]
-
-test_img_path = ['../dataset/test_img/' +test_img[i].split("/")[-1] for i in range(len(test_img))]
-test_label_path = ['../dataset/test_label/' +test_label[i].split("/")[-1] for i in range(len(test_label))]
-
-for i in range(len(test_img)):
-    shutil.move(test_img[i], test_img_path[i])
-
-for i in range(len(test_label)):
-    shutil.move(test_label[i], test_label_path[i])
\ No newline at end of file
diff --git a/demo.py b/demo.py
deleted file mode 100644
index 98146c7..0000000
--- a/demo.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import cv2
-import time
-import tensorflow as tf
-from model.pspunet import pspunet
-from data_loader.display import create_mask
-import numpy as np
-gpus = tf.config.experimental.list_physical_devices('GPU')
-
-if gpus:
-    try:
-        tf.config.experimental.set_virtual_device_configuration(
-       gpus[0],
-        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=500)])
-    except RuntimeError as e:
-        print(e)
-        
-cap= cv2.VideoCapture(YOUR_VIDEO_PATH)
-
-IMG_WIDTH = 480
-IMG_HEIGHT = 272
-n_classes = 7
-
-model = pspunet((IMG_HEIGHT, IMG_WIDTH ,3), n_classes)
-model.load_weights("pspunet_weight.h5")
-
-while True:
-    start= time.time()
-    try:
-        _,frame = cap.read()
-        frame = cv2.resize(frame, (IMG_WIDTH, IMG_HEIGHT))
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        frame = frame[tf.newaxis, ...]
-        frame = frame/255
-    except:
-        cv2.destroyAllWindows()
-        cap.release()
-        break
-        
-    pre = model.predict(frame)
-    pre = create_mask(pre).numpy()
-
-    frame2 = frame/2
-    frame2[0][(pre==1).all(axis=2)] += [0, 0, 0] #""bike_lane_normal", "sidewalk_asphalt", "sidewalk_urethane""
-    frame2[0][(pre==2).all(axis=2)] += [0.5, 0.5,0] # "caution_zone_stairs", "caution_zone_manhole", "caution_zone_tree_zone", "caution_zone_grating", "caution_zone_repair_zone"]
-    frame2[0][(pre==3).all(axis=2)] += [0.2, 0.7, 0.5] #"alley_crosswalk","roadway_crosswalk"
-    frame2[0][(pre==4).all(axis=2)] += [0, 0.5, 0.5] #"braille_guide_blocks_normal", "braille_guide_blocks_damaged"
-    frame2[0][(pre==5).all(axis=2)] += [0, 0, 0.5] #"roadway_normal","alley_normal","alley_speed_bump", "alley_damaged""
-    frame2[0][(pre==6).all(axis=2)] += [0.5, 0, 0] #"sidewalk_blocks","sidewalk_cement" , "sidewalk_soil_stone", "sidewalk_damaged","sidewalk_other"
-    video = np.uint8(frame2)
-    
-    print(1/(time.time()-start))
-    cv2.waitKey(1)
-
diff --git a/launch/road_segmentation.launch b/launch/road_segmentation.launch
new file mode 100644
index 0000000..36d6370
--- /dev/null
+++ b/launch/road_segmentation.launch
@@ -0,0 +1,21 @@
+<?xml version="1.0"?>
+<launch>
+  <!-- Argument for camera topic, default is set to "/camera/image_raw" -->
+  <arg name="camera_topic" default="/camera/image_raw"/>
+  <arg name="camera_info_topic" default="/camera/camera_info"/>
+  <arg name="debug" default="true"/>
+
+  <!-- Model path argument -->
+  <arg name="model_path" default="$(find road_segmentation)/models/pspunet_weight.h5"/>
+
+  <!-- Load the class.yaml file contents into ROS parameter server -->
+  <rosparam file="$(find road_segmentation)/config/class.yaml" command="load"/>
+
+  <!-- Node execution -->
+  <node pkg="road_segmentation" type="road_segmentation_node.py" name="road_segmentation_node" output="screen">
+    <param name="camera_topic" value="$(arg camera_topic)"/>
+    <param name="camera_info_topic" value="$(arg camera_info_topic)"/>
+    <param name="model_path" value="$(arg model_path)"/>
+    <param name="debug" value="$(arg debug)"/>
+  </node>
+</launch>
diff --git a/launch/usb_camera.launch b/launch/usb_camera.launch
new file mode 100644
index 0000000..638acdc
--- /dev/null
+++ b/launch/usb_camera.launch
@@ -0,0 +1,16 @@
+<?xml version="1.0"?>
+<launch>
+  <!-- Start USB camera node -->
+  <node name="usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen">
+    <!-- Set the image topic to match the one used in road_segmentation -->
+    <param name="video_device" value="/dev/video0"/>
+    <param name="image_width" value="640"/>
+    <param name="image_height" value="480"/>
+    <param name="framerate" value="30"/>
+    <param name="pixel_format" value="yuyv"/>
+    <param name="camera_name" value="usb_cam"/>
+    <param name="camera_frame_id" value="usb_cam_frame"/>
+    <remap from="~image_raw" to="/camera/image_raw"/>
+    <remap from="~camera_info" to="/camera/camera_info"/>
+  </node>
+</launch>
diff --git a/pspunet_weight.h5 b/models/pspunet_weight.h5
similarity index 100%
rename from pspunet_weight.h5
rename to models/pspunet_weight.h5
diff --git a/icnet_train.ipynb b/notebooks/icnet_train.ipynb
similarity index 100%
rename from icnet_train.ipynb
rename to notebooks/icnet_train.ipynb
diff --git a/train.ipynb b/notebooks/train.ipynb
similarity index 100%
rename from train.ipynb
rename to notebooks/train.ipynb
diff --git a/package.xml b/package.xml
new file mode 100644
index 0000000..5d20dd6
--- /dev/null
+++ b/package.xml
@@ -0,0 +1,23 @@
+<?xml version="1.0"?>
+<package format="2">
+  <name>road_segmentation</name>
+  <version>0.0.0</version>
+  <description>The road_segmentation package</description>
+  <maintainer email="SBGRP-git@g.softbank.co.jp">SoftBank corp.</maintainer>
+  <license>Apache 2.0</license>
+  <buildtool_depend>catkin</buildtool_depend>
+
+  <build_depend>rospy</build_depend>
+  <build_depend>std_msgs</build_depend>
+  <build_depend>sensor_msgs</build_depend>
+  <build_depend>cv_bridge</build_depend>
+  <build_depend>jsk_recognition_msgs</build_depend>
+
+  <exec_depend>rospy</exec_depend>
+  <exec_depend>std_msgs</exec_depend>
+  <exec_depend>sensor_msgs</exec_depend>
+  <exec_depend>cv_bridge</exec_depend>
+  <exec_depend>jsk_recognition_msgs</exec_depend>
+  <exec_depend>usb_cam</exec_depend>
+
+</package>
diff --git a/scripts/road_segmentation_node.py b/scripts/road_segmentation_node.py
new file mode 100755
index 0000000..45f7db8
--- /dev/null
+++ b/scripts/road_segmentation_node.py
@@ -0,0 +1,177 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
+
+import os
+
+import cv2
+import numpy as np
+import rospy
+import tensorflow as tensorflow
+from cv_bridge import CvBridge
+from cv_bridge import CvBridgeError
+from sensor_msgs.msg import CameraInfo
+from sensor_msgs.msg import Image
+
+from road_segmentation.data_loader.display import create_mask
+
+bridge = CvBridge()
+
+
+class RoadSegmentationNode:
+    def __init__(self):
+        rospy.init_node('road_segmentation_node')
+
+        # GPU configuration
+        self.gpus = tensorflow.config.experimental.list_physical_devices('GPU')
+        if self.gpus:
+            try:
+                tensorflow.config.experimental.set_virtual_device_configuration(
+                    self.gpus[0],
+                    [tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=500)]
+                )
+            except RuntimeError as e:
+                rospy.logerr("Error configuring GPU: {}".format(e))
+
+        # Set default model path to pspunet_weight.h5 in the models directory
+        default_model_path = os.path.join(
+            rospy.get_param('~model_dir', os.path.dirname(__file__) + '/../models'),
+            'pspunet_weight.h5'
+        )
+        self.camera_topic = rospy.get_param('~camera_topic', '/camera/image_raw')
+        self.camera_info_topic = rospy.get_param('~camera_info_topic', '/camera/camera_info')
+        self.model_path = rospy.get_param('~model_path', default_model_path)
+        self.debug = rospy.get_param('~debug', True)
+
+        # Load class names and labels from ROS parameter
+        class_data = rospy.get_param('class')
+        self.class_names = list(class_data.keys())
+        self.num_classes = len(self.class_names)
+
+        # Load the segmentation model
+        self.model = tensorflow.keras.models.load_model(self.model_path)
+
+        # Get input image size from model
+        self.input_image_size = self.model.input_shape[1:3]  # (height, width)
+
+        # Subscribe to the camera and camera_info topics
+        self.image_sub = rospy.Subscriber(self.camera_topic, Image, self.image_callback)
+        self.camera_info_sub = rospy.Subscriber(self.camera_info_topic, CameraInfo, self.camera_info_callback)
+
+        # Publisher for adjusted camera info and the segmentation result for each class
+        self.camera_info_pub = rospy.Publisher('~result/camera_info', CameraInfo, queue_size=1)
+        # Publisher for the segmentation result for each class
+        self.result_pubs = []
+        for class_name in self.class_names:
+            pub = rospy.Publisher(f'~result/{class_name}_mask', Image, queue_size=1)
+            self.result_pubs.append(pub)
+
+        # Publisher for debug image (only if debug mode is enabled)
+        if self.debug:
+            self.debug_image_pub = rospy.Publisher('~debug_image', Image, queue_size=1)
+
+        self.current_camera_info = None
+        rospy.loginfo("Road Segmentation Node Initialized")
+
+    def camera_info_callback(self, msg):
+        self.current_camera_info = msg
+
+    def perform_segmentation(self, image):
+        # Resize the input image to the size expected by the model
+        input_image = cv2.resize(image, (self.input_image_size[1], self.input_image_size[0]))  # (width, height)
+        input_image = np.expand_dims(input_image, axis=0)
+        input_image = input_image / 255.0  # Normalize input
+
+        # Get the segmentation result (class map)
+        result = self.model.predict(input_image)
+
+        # Convert the result to a mask image using create_mask
+        result_mask = create_mask(result).numpy()
+
+        return result_mask
+
+    def create_debug_image(self, image, result_mask):
+        # Apply colormap to visualize the mask
+        result_mask_colored = cv2.applyColorMap((result_mask * 36).astype(np.uint8), cv2.COLORMAP_JET)
+
+        # Overlay the segmentation result on the original image with transparency
+        alpha = 0.6
+        debug_image = cv2.addWeighted(image, alpha, result_mask_colored, 1 - alpha, 0)
+
+        return debug_image
+
+    def publish_debug_image(self, debug_image):
+        try:
+            # Convert the CV2 image back to a ROS Image message
+            debug_image_msg = bridge.cv2_to_imgmsg(debug_image, encoding='bgr8')
+            self.debug_image_pub.publish(debug_image_msg)
+        except CvBridgeError as e:
+            rospy.logerr("CvBridge Error: {0}".format(e))
+
+    def adjust_camera_info(self, original_info, target_width, target_height):
+        scale_x = target_width / float(original_info.width)
+        scale_y = target_height / float(original_info.height)
+
+        adjusted_info = CameraInfo()
+        adjusted_info.header = original_info.header
+        adjusted_info.width = target_width
+        adjusted_info.height = target_height
+        adjusted_info.K = [scale_x * original_info.K[0], 0, scale_x * original_info.K[2],
+                           0, scale_y * original_info.K[4], scale_y * original_info.K[5],
+                           0, 0, 1]
+        adjusted_info.P = [scale_x * original_info.P[0], 0, scale_x * original_info.P[2], 0,
+                           0, scale_y * original_info.P[5], scale_y * original_info.P[6], 0,
+                           0, 0, 1, 0]
+        adjusted_info.D = original_info.D
+        adjusted_info.R = original_info.R
+
+        return adjusted_info
+
+    def image_callback(self, msg):
+        try:
+            # Convert the ROS Image message to a CV2 image
+            cv_image = bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
+        except CvBridgeError as e:
+            rospy.logerr("CvBridge Error: {0}".format(e))
+            return
+
+        # Perform segmentation on the received image
+        result_mask = self.perform_segmentation(cv_image)
+
+        # Resize the entire mask to match the original image size
+        result_mask_resized = cv2.resize(
+            result_mask,
+            (cv_image.shape[1],
+             cv_image.shape[0]),
+            interpolation=cv2.INTER_NEAREST)
+
+        # Adjust the camera info for the resized mask
+        if self.current_camera_info is not None:
+            adjusted_camera_info = self.adjust_camera_info(
+                self.current_camera_info, cv_image.shape[1], cv_image.shape[0])
+            self.camera_info_pub.publish(adjusted_camera_info)
+
+        # Publish the mask for each class
+        for i in range(self.num_classes):
+            class_mask = (result_mask_resized == i).astype(np.uint8) * 255
+            self.publish_class_mask(class_mask, self.result_pubs[i])
+
+        # If debug mode is enabled, publish the debug image
+        if self.debug:
+            debug_image = self.create_debug_image(cv_image, result_mask_resized)
+            self.publish_debug_image(debug_image)
+
+    def publish_class_mask(self, class_mask, pub):
+        try:
+            # Convert the CV2 image back to a ROS Image message
+            mask_msg = bridge.cv2_to_imgmsg(class_mask, encoding='mono8')
+            pub.publish(mask_msg)
+        except CvBridgeError as e:
+            rospy.logerr("CvBridge Error: {0}".format(e))
+
+
+if __name__ == '__main__':
+    try:
+        node = RoadSegmentationNode()
+        rospy.spin()
+    except rospy.ROSInterruptException:
+        pass
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..c40d52c
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,10 @@
+from distutils.core import setup
+from catkin_pkg.python_setup import generate_distutils_setup
+
+d = generate_distutils_setup(
+    packages=['road_segmentation'],
+    package_dir={'': 'src'}
+)
+
+setup(**d)
+
diff --git a/src/road_segmentation/__init__.py b/src/road_segmentation/__init__.py
new file mode 100644
index 0000000..4b2bbf5
--- /dev/null
+++ b/src/road_segmentation/__init__.py
@@ -0,0 +1,2 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
diff --git a/src/road_segmentation/__pycache__/model_loader.cpython-38.pyc b/src/road_segmentation/__pycache__/model_loader.cpython-38.pyc
new file mode 100644
index 0000000..dde2bf1
Binary files /dev/null and b/src/road_segmentation/__pycache__/model_loader.cpython-38.pyc differ
diff --git a/data_loader/data_loader.py b/src/road_segmentation/data_loader/data_loader.py
similarity index 98%
rename from data_loader/data_loader.py
rename to src/road_segmentation/data_loader/data_loader.py
index 250b475..9590291 100644
--- a/data_loader/data_loader.py
+++ b/src/road_segmentation/data_loader/data_loader.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import os
 import cv2
 import numpy as np
diff --git a/data_loader/display.py b/src/road_segmentation/data_loader/display.py
similarity index 75%
rename from data_loader/display.py
rename to src/road_segmentation/data_loader/display.py
index c50989c..fd0493d 100644
--- a/data_loader/display.py
+++ b/src/road_segmentation/data_loader/display.py
@@ -1,27 +1,32 @@
-from IPython.display import clear_output
-from data_loader import *
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import matplotlib.pyplot as plt
 import tensorflow as tf
+from IPython.display import clear_output
+
+from .data_loader import *
 
 
 def display(display_list):
     plt.figure(figsize=(7, 7))
     for i in range(3):
-        plt.subplot(3, 3, i*3+1)
-        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i][0]/255))
+        plt.subplot(3, 3, i * 3 + 1)
+        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i][0] / 255))
         plt.axis('off')
-        plt.subplot(3, 3, i*3+2)
-        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i][1]/255))
+        plt.subplot(3, 3, i * 3 + 2)
+        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i][1] / 255))
         plt.axis('off')
-        plt.subplot(3, 3, i*3+3)
-        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i][2]/255))
+        plt.subplot(3, 3, i * 3 + 3)
+        plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i][2] / 255))
         plt.axis('off')
     plt.show()
 
+
 def create_mask(pred_mask):
     pred_mask = tf.argmax(pred_mask, axis=-1)
     pred_mask = pred_mask[..., tf.newaxis]
-    return pred_mask[0]   
+    return pred_mask[0]
+
 
 def show_predictions(image, label, model):
 
@@ -29,7 +34,6 @@ def show_predictions(image, label, model):
         pred_mask = [model.predict(image[tf.newaxis, ...]) for image in image]
         display_list = [[image[i], label[i], create_mask(pred_mask[i])] for i in range(3)]
         display(display_list)
-    else :  
+    else:
         display_list = [[image[i], label[i], image[i]] for i in range(3)]
         display(display_list)
-        
\ No newline at end of file
diff --git a/src/road_segmentation/data_loader/split_train_test.py b/src/road_segmentation/data_loader/split_train_test.py
new file mode 100644
index 0000000..dd3eb34
--- /dev/null
+++ b/src/road_segmentation/data_loader/split_train_test.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
+import shutil
+
+from .data_loader import mask_images
+from .data_loader import train_images
+
+train_images = train_images()
+mask_images = mask_images()
+
+train_num = int(len(train_images) * 0.75)
+
+test_img = train_images[train_num:]
+test_label = mask_images[train_num:]
+
+test_img_path = ['../dataset/test_img/' + test_img[i].split("/")[-1] for i in range(len(test_img))]
+test_label_path = ['../dataset/test_label/' + test_label[i].split("/")[-1] for i in range(len(test_label))]
+
+for i in range(len(test_img)):
+    shutil.move(test_img[i], test_img_path[i])
+
+for i in range(len(test_label)):
+    shutil.move(test_label[i], test_label_path[i])
diff --git a/model/Deeplab_v3.py b/src/road_segmentation/model/Deeplab_v3.py
similarity index 99%
rename from model/Deeplab_v3.py
rename to src/road_segmentation/model/Deeplab_v3.py
index f687e88..98e9f1b 100644
--- a/model/Deeplab_v3.py
+++ b/src/road_segmentation/model/Deeplab_v3.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import os
 import math
 import keras
diff --git a/model/fcn.py b/src/road_segmentation/model/fcn.py
similarity index 98%
rename from model/fcn.py
rename to src/road_segmentation/model/fcn.py
index bb0d9c5..a1be181 100644
--- a/model/fcn.py
+++ b/src/road_segmentation/model/fcn.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 from tensorflow.keras import Sequential
 from tensorflow.keras.applications.vgg16 import VGG16
diff --git a/model/icnet.py b/src/road_segmentation/model/icnet.py
similarity index 99%
rename from model/icnet.py
rename to src/road_segmentation/model/icnet.py
index 75cd1de..4fc767a 100644
--- a/model/icnet.py
+++ b/src/road_segmentation/model/icnet.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras import layers
diff --git a/model/pspnet.py b/src/road_segmentation/model/pspnet.py
similarity index 98%
rename from model/pspnet.py
rename to src/road_segmentation/model/pspnet.py
index 4334d16..663ff87 100644
--- a/model/pspnet.py
+++ b/src/road_segmentation/model/pspnet.py
@@ -1,4 +1,5 @@
-
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras import layers
diff --git a/model/pspunet.py b/src/road_segmentation/model/pspunet.py
similarity index 99%
rename from model/pspunet.py
rename to src/road_segmentation/model/pspunet.py
index 5401b98..9ca6879 100644
--- a/model/pspunet.py
+++ b/src/road_segmentation/model/pspunet.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 from tensorflow.keras import Sequential
 import tensorflow as tf
diff --git a/model/pspunet_vgg16.py b/src/road_segmentation/model/pspunet_vgg16.py
similarity index 98%
rename from model/pspunet_vgg16.py
rename to src/road_segmentation/model/pspunet_vgg16.py
index 62d8b9b..dc8af2a 100644
--- a/model/pspunet_vgg16.py
+++ b/src/road_segmentation/model/pspunet_vgg16.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 from tensorflow.keras import Sequential
 import tensorflow as tf
diff --git a/model/unet.py b/src/road_segmentation/model/unet.py
similarity index 98%
rename from model/unet.py
rename to src/road_segmentation/model/unet.py
index db1403b..f01bf90 100644
--- a/model/unet.py
+++ b/src/road_segmentation/model/unet.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 from tensorflow.keras import Sequential
 import tensorflow as tf
diff --git a/model/unet_crop_copy.py b/src/road_segmentation/model/unet_crop_copy.py
similarity index 98%
rename from model/unet_crop_copy.py
rename to src/road_segmentation/model/unet_crop_copy.py
index 8526848..94acc91 100644
--- a/model/unet_crop_copy.py
+++ b/src/road_segmentation/model/unet_crop_copy.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env pipenv-shebang
+# -*- coding:utf-8 -*-
 import numpy as np
 from tensorflow.keras import Sequential
 import tensorflow as tf