diff --git a/README.md b/README.md
index 604c67e..4c2f7b3 100644
--- a/README.md
+++ b/README.md
@@ -12,9 +12,20 @@ To run Default SSD (Single Shot Detection) algorithm:
 
 1. Install ROS: http://wiki.ros.org/kinetic/Installation/Ubuntu
 
-2. Install camera dependencies
+2. Install vision_msgs Dependency
+    `sudo apt install ros-kinetic-vision-msgs`
 
-    `sudo apt-get install ros-kinetic-usb_cam ros-kinetic-openni2-launch`
+3. Install any one of the following camera dependencies
+
+    `sudo apt install ros-kinetic-openni2-launch` (for openni2 supported camera)
+
+                OR
+
+    `sudo apt install ros-kinetic-usb-cam`  (usb camera / webcam)
+
+                OR
+
+    `sudo apt install ros-kinetic-cv-camera`  (usb camera / webcam)
 
 3. Install tensorflow into python virtualenv: https://www.tensorflow.org/install/install_linux
 
@@ -34,11 +45,9 @@ To run Default SSD (Single Shot Detection) algorithm:
 
     `cd ~/catkin_ws/src`
 
-    `git clone https://github.com/Kukanani/vision_msgs.git`
-
     `git clone https://github.com/osrf/tensorflow_object_detector.git`
 
-6. Build tensorflow_object_detector and Vision message 
+6. Build tensorflow_object_detector and Vision message
 
     `cd ~/catkin_ws && catkin_make`
 
@@ -54,11 +63,12 @@ To run Default SSD (Single Shot Detection) algorithm:
 
     `roslaunch tensorflow_object_detector usb_cam_detector.launch`
 
+    OR
 
+    `roslaunch tensorflow_object_detector cv_camera_detector.launch`
 
 If you want to try any other ML model:
 
-1. Download any Object Detection Models from the Tensorflow Object detection API and place it in `data/models/`. You can find the models in tensorflow Object Detection Model Zoo: https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md. Extract the `tar.gz` file.
-
-2. Edit the MODEL_NAME and LABEL_NAME in detect_ros.py. By default it is `ssd_mobilenet_v1_coco_11_06_2017` with `mscoco_label_map.pbtxt` respectively. 
+1. Download any Object Detection Models from the Tensorflow Object detection API. You can find the models in tensorflow Object Detection Model Zoo: https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md. Extract the `tar.gz` file.
 
+2. Edit the parameters in any of the launch file. If no parameters are provided then, `ssd_mobilenet_v2_coco_2018_03_29` is downloaded and stored in `~/data/models`.
diff --git a/CMakeLists.txt b/detector/CMakeLists.txt
similarity index 90%
rename from CMakeLists.txt
rename to detector/CMakeLists.txt
index 7333b48..aa04e6a 100644
--- a/CMakeLists.txt
+++ b/detector/CMakeLists.txt
@@ -17,27 +17,7 @@ find_package(catkin REQUIRED COMPONENTS
 )
 
 ##set the URL to download
-set(URL "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz")
-set(DOWNLOAD_PATH ${CMAKE_CURRENT_SOURCE_DIR}/data/models/ssd_mobilenet_v1_coco_11_06_2017.tar.gz)
-set(EXTRACTED_PATH ${CMAKE_CURRENT_SOURCE_DIR}/data/models)
-
-if (NOT EXISTS "${EXTRACTED_PATH}/ssd_mobilenet_v1_coco_11_06_2017")
-  if (NOT EXISTS "${DOWNLOAD_PATH}")
-  message(STATUS "Downloading SSD model ....")
-  file(
-    DOWNLOAD "${URL}" "${DOWNLOAD_PATH}"
-    SHOW_PROGRESS
-  )
-  endif()
-  message(STATUS "Extracting...")
-  execute_process(
-    COMMAND cmake -E tar xzf ${DOWNLOAD_PATH}
-    WORKING_DIRECTORY ${EXTRACTED_PATH}
-  )
-endif()
-
-file(REMOVE ${DOWNLOAD_PATH})
-	
+
 ## System dependencies are found with CMake's conventions
 # find_package(Boost REQUIRED COMPONENTS system)
 
@@ -133,7 +113,7 @@ catkin_package(
 #  DEPENDS system_lib
 )
 
-catkin_install_python(PROGRAMS scripts/detect_ros.py 
+catkin_install_python(PROGRAMS scripts/detect_ros.py
 			DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION})
 ###########
 ## Build ##
diff --git a/config/display.rviz b/detector/config/display.rviz
similarity index 100%
rename from config/display.rviz
rename to detector/config/display.rviz
diff --git a/data/.gitignore b/detector/data/.gitignore
similarity index 100%
rename from data/.gitignore
rename to detector/data/.gitignore
diff --git a/data/labels/mscoco_label_map.pbtxt b/detector/data/labels/mscoco_label_map.pbtxt
similarity index 100%
rename from data/labels/mscoco_label_map.pbtxt
rename to detector/data/labels/mscoco_label_map.pbtxt
diff --git a/data/labels/pascal_label_map.pbtxt b/detector/data/labels/pascal_label_map.pbtxt
similarity index 100%
rename from data/labels/pascal_label_map.pbtxt
rename to detector/data/labels/pascal_label_map.pbtxt
diff --git a/data/labels/pet_label_map.pbtxt b/detector/data/labels/pet_label_map.pbtxt
similarity index 100%
rename from data/labels/pet_label_map.pbtxt
rename to detector/data/labels/pet_label_map.pbtxt
diff --git a/data/models/readme.md b/detector/data/models/readme.md
similarity index 100%
rename from data/models/readme.md
rename to detector/data/models/readme.md
diff --git a/detector/launch/cv_camera_detector.launch b/detector/launch/cv_camera_detector.launch
new file mode 100644
index 0000000..a2f5901
--- /dev/null
+++ b/detector/launch/cv_camera_detector.launch
@@ -0,0 +1,17 @@
+<launch>
+	<node pkg= "tensorflow_object_detector" name="detect_ros" type="detect_ros.py"  output="screen">
+    <remap from="image" to="/cv_camera_node/image_raw"/>
+
+		<param name="model_path" value=""/>
+		<param name="labels_path" value=""/>
+		<param name="num_classes" value="90"/>
+	</node>
+
+  <node pkg="cv_camera" type="cv_camera_node" name="cv_camera_node" output="screen">
+    <param name="device_id" value="0"/>
+  </node>
+
+  <node pkg="image_view" type="image_view" name="image_view">
+    <remap from="image" to="debug_image"/>
+  </node>
+</launch>
diff --git a/launch/object_detect.launch b/detector/launch/object_detect.launch
similarity index 75%
rename from launch/object_detect.launch
rename to detector/launch/object_detect.launch
index 0a4c837..d5fc94b 100644
--- a/launch/object_detect.launch
+++ b/detector/launch/object_detect.launch
@@ -1,6 +1,10 @@
 <launch>
-	<node pkg= "tensorflow_object_detector" name="detect_ros" type="detect_ros.py"  output="screen"> 
+	<node pkg= "tensorflow_object_detector" name="detect_ros" type="detect_ros.py"  output="screen">
     <remap from='image' to='/camera/rgb/image_raw'/>
+
+		<param name="model_path" value=""/>
+		<param name="labels_path" value=""/>
+		<param name="num_classes" value="90"/>
 	</node>
 	<!-- rviz -->
   <arg name ="rviz" default="true" />
diff --git a/launch/usb_cam_detector.launch b/detector/launch/usb_cam_detector.launch
similarity index 74%
rename from launch/usb_cam_detector.launch
rename to detector/launch/usb_cam_detector.launch
index e1e4135..6580bc5 100644
--- a/launch/usb_cam_detector.launch
+++ b/detector/launch/usb_cam_detector.launch
@@ -1,6 +1,10 @@
 <launch>
-	<node pkg= "tensorflow_object_detector" name="detect_ros" type="detect_ros.py"  output="screen"> 
+	<node pkg= "tensorflow_object_detector" name="detect_ros" type="detect_ros.py"  output="screen">
     <remap from="image" to="/usb_cam_node/image_raw"/>
+
+		<param name="model_path" value=""/>
+		<param name="labels_path" value=""/>
+		<param name="num_classes" value="90"/>
 	</node>
 
   <node pkg="usb_cam" type="usb_cam_node" name="usb_cam_node" output="screen">
diff --git a/package.xml b/detector/package.xml
similarity index 100%
rename from package.xml
rename to detector/package.xml
diff --git a/detector/scripts/detect_ros.py b/detector/scripts/detect_ros.py
new file mode 100755
index 0000000..006eccf
--- /dev/null
+++ b/detector/scripts/detect_ros.py
@@ -0,0 +1,225 @@
+#!/usr/bin/env python
+## Author: Rohit
+## Date: July, 25, 2017
+# Purpose: Ros node to detect objects using tensorflow
+
+import os
+import sys
+import cv2
+import tarfile
+import numpy as np
+try:
+    import tensorflow as tf
+except ImportError:
+    print("unable to import TensorFlow. Is it installed?")
+    print("  sudo apt install python-pip")
+    print("  sudo pip install tensorflow")
+    sys.exit(1)
+
+# ROS related imports
+import rospy
+from std_msgs.msg import String , Header
+from sensor_msgs.msg import Image
+from cv_bridge import CvBridge, CvBridgeError
+from vision_msgs.msg import Detection2D, Detection2DArray, ObjectHypothesisWithPose
+
+# Object detection module imports
+import object_detection
+from object_detection.utils import label_map_util
+from object_detection.utils import visualization_utils as vis_util
+import urllib2
+
+DEFAULT_MODEL = os.path.join(os.path.expanduser("~"), "data", "models", "ssd_mobilenet_v1_coco_2018_01_28", "frozen_inference_graph.pb")
+DEFAULT_LABELS = os.path.join(os.path.dirname(sys.path[0]), "data", "labels", "mscoco_label_map.pbtxt")
+
+def download_model(url, location):
+
+    file_name = url.split('/')[-1]
+    u = urllib2.urlopen(url)
+    if not os.path.exists(location):
+        os.makedirs(location)
+
+    file_path = os.path.join(location, os.path.basename(url))
+    f = open(file_path, 'wb')
+    meta = u.info()
+    file_size = int(meta.getheaders("Content-Length")[0])
+    print "Downloading: %s Bytes: %s" % (file_name, file_size)
+
+    file_size_dl = 0
+    block_sz = 8192
+    while True:
+        buffer = u.read(block_sz)
+        if not buffer:
+            break
+
+        file_size_dl += len(buffer)
+        f.write(buffer)
+        status = r"%10d  [%3.2f%%]" % (file_size_dl, file_size_dl * 100. / file_size)
+        status = status + chr(8)*(len(status)+1)
+        print status,
+
+    f.close()
+
+    return file_path
+
+def download_data():
+    ## Downloading COCO Trained Model
+    if not os.path.exists(DEFAULT_MODEL):
+        model_path = os.path.join(os.path.expanduser("~"), "data", "models")
+        final_path = download_model("http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz", model_path)
+        print final_path
+        tar = tarfile.open(final_path)
+        tar.extractall(path=os.path.dirname(final_path))
+        tar.close()
+
+def get_model_params():
+    model_path = rospy.get_param("~model_path")
+    labels_path = rospy.get_param("~labels_path")
+
+
+    if (not model_path and not labels_path):
+        rospy.logwarn("No params passed, using default model")
+        download_data()
+        return (DEFAULT_MODEL, DEFAULT_LABELS)
+
+    elif (os.path.exists(os.path.join(model_path, "frozen_inference_graph.pb")) and os.path.exists(labels_path)):
+        rospy.loginfo("Using Passed parameters")
+        return (os.path.join(model_path, "frozen_inference_graph.pb"), labels_path)
+
+    else:
+        raise Exception("Either Incomplete arguments were passed or the paths do not exist. To use the default model do not pass any parameters. NOTE: Please use absolute paths in params")
+
+# Detection
+
+class Detector:
+
+    def __init__(self):
+
+        ######### Set model here ############
+        path_to_ckpt, path_to_labels = get_model_params()
+
+        num_classes = rospy.get_param("~num_classes")
+
+        detection_graph = tf.Graph()
+        with detection_graph.as_default():
+            od_graph_def = tf.GraphDef()
+            with tf.gfile.GFile(path_to_ckpt, 'rb') as fid:
+                serialized_graph = fid.read()
+                od_graph_def.ParseFromString(serialized_graph)
+                tf.import_graph_def(od_graph_def, name='')
+
+        ## Loading label map
+        # Label maps map indices to category names, so that when our convolution network predicts `5`,
+        # we know that this corresponds to `airplane`.  Here we use internal utility functions,
+        # but anything that returns a dictionary mapping integers to appropriate string labels would be fine
+        label_map = label_map_util.load_labelmap(path_to_labels)
+        categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=num_classes, use_display_name=True)
+        self.category_index = label_map_util.create_category_index(categories)
+
+        # Setting the GPU options to use fraction of gpu that has been set
+        # config = tf.ConfigProto()
+        # config.gpu_options.per_process_gpu_memory_fraction = GPU_FRACTION
+        ops = detection_graph.get_operations()
+        all_tensor_names = {output.name for op in ops for output in op.outputs}
+        self.tensor_dict = {}
+        for key in [
+            'num_detections', 'detection_boxes', 'detection_scores',
+            'detection_classes', 'detection_masks'
+        ]:
+            tensor_name = key + ':0'
+            if tensor_name in all_tensor_names:
+                self.tensor_dict[key] = detection_graph.get_tensor_by_name(
+                    tensor_name)
+
+        self.image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+
+        self.sess = tf.Session(graph=detection_graph)
+
+        rospy.loginfo("Initializing")
+
+        dummy_tensor = np.zeros((1,1,1,3), dtype=np.int32)
+        self.sess.run(self.tensor_dict,
+                               feed_dict={self.image_tensor: dummy_tensor})
+
+        self.image_pub = rospy.Publisher("debug_image",Image, queue_size=1)
+        self.object_pub = rospy.Publisher("objects", Detection2DArray, queue_size=1)
+        self.bridge = CvBridge()
+        self.image_sub = rospy.Subscriber("image", Image, self.image_cb, queue_size=1, buff_size=2**24)
+
+
+    def image_cb(self, data):
+        objArray = Detection2DArray()
+        try:
+            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
+        except CvBridgeError as e:
+            print(e)
+        image=cv2.cvtColor(cv_image,cv2.COLOR_BGR2RGB)
+
+        # the array based representation of the image will be used later in order to prepare the
+        # result image with boxes and labels on it.
+        image_np = np.asarray(image)
+        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+        image_np_expanded = np.expand_dims(image_np, axis=0)
+
+        output_dict = self.sess.run(self.tensor_dict,
+                                    feed_dict={self.image_tensor: image_np_expanded})
+
+        objects=vis_util.visualize_boxes_and_labels_on_image_array(
+            image,
+            np.squeeze(output_dict["detection_boxes"]),
+            np.squeeze(output_dict["detection_classes"]).astype(np.int32),
+            np.squeeze(output_dict["detection_scores"]),
+            self.category_index,
+            use_normalized_coordinates=True,
+            line_thickness=2)
+
+        objArray.detections =[]
+        objArray.header=data.header
+        object_count=1
+
+        for i in range(len(objects)):
+            object_count+=1
+            objArray.detections.append(self.object_predict(objects[i],data.header,image_np,cv_image))
+
+        self.object_pub.publish(objArray)
+
+        img=cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+        image_out = Image()
+        try:
+            image_out = self.bridge.cv2_to_imgmsg(img,"bgr8")
+        except CvBridgeError as e:
+            print(e)
+        image_out.header = data.header
+        self.image_pub.publish(image_out)
+
+    def object_predict(self,object_data, header, image_np,image):
+        image_height,image_width,channels = image.shape
+        obj=Detection2D()
+        obj_hypothesis= ObjectHypothesisWithPose()
+
+        object_id=object_data[0]
+        object_score=object_data[1]
+        dimensions=object_data[2]
+
+        obj.header=header
+        obj_hypothesis.id = object_id
+        obj_hypothesis.score = object_score
+        obj.results.append(obj_hypothesis)
+        obj.bbox.size_y = int((dimensions[2]-dimensions[0])*image_height)
+        obj.bbox.size_x = int((dimensions[3]-dimensions[1] )*image_width)
+        obj.bbox.center.x = int((dimensions[1] + dimensions [3])*image_width/2)
+        obj.bbox.center.y = int((dimensions[0] + dimensions[2])*image_height/2)
+
+        return obj
+
+def main(args):
+    rospy.init_node('detector_node')
+    obj=Detector()
+    try:
+        rospy.spin()
+    except KeyboardInterrupt:
+        print("ShutDown")
+    cv2.destroyAllWindows()
+
+if __name__=='__main__':
+    main(sys.argv)
diff --git a/setup.py b/detector/setup.py
similarity index 100%
rename from setup.py
rename to detector/setup.py
diff --git a/src/object_detection/__init__.py b/detector/src/object_detection/__init__.py
similarity index 100%
rename from src/object_detection/__init__.py
rename to detector/src/object_detection/__init__.py
diff --git a/src/object_detection/anchor_generators/__init__.py b/detector/src/object_detection/protos/__init__.py
similarity index 100%
rename from src/object_detection/anchor_generators/__init__.py
rename to detector/src/object_detection/protos/__init__.py
diff --git a/src/object_detection/protos/string_int_label_map.proto b/detector/src/object_detection/protos/string_int_label_map.proto
similarity index 100%
rename from src/object_detection/protos/string_int_label_map.proto
rename to detector/src/object_detection/protos/string_int_label_map.proto
diff --git a/src/object_detection/protos/string_int_label_map_pb2.py b/detector/src/object_detection/protos/string_int_label_map_pb2.py
similarity index 100%
rename from src/object_detection/protos/string_int_label_map_pb2.py
rename to detector/src/object_detection/protos/string_int_label_map_pb2.py
diff --git a/src/object_detection/box_coders/__init__.py b/detector/src/object_detection/utils/__init__.py
similarity index 100%
rename from src/object_detection/box_coders/__init__.py
rename to detector/src/object_detection/utils/__init__.py
diff --git a/src/object_detection/utils/label_map_util.py b/detector/src/object_detection/utils/label_map_util.py
similarity index 100%
rename from src/object_detection/utils/label_map_util.py
rename to detector/src/object_detection/utils/label_map_util.py
diff --git a/src/object_detection/utils/visualization_utils.py b/detector/src/object_detection/utils/visualization_utils.py
similarity index 100%
rename from src/object_detection/utils/visualization_utils.py
rename to detector/src/object_detection/utils/visualization_utils.py
diff --git a/scripts/detect_ros.py b/scripts/detect_ros.py
deleted file mode 100755
index 09dd64d..0000000
--- a/scripts/detect_ros.py
+++ /dev/null
@@ -1,160 +0,0 @@
-#!/usr/bin/env python
-## Author: Rohit
-## Date: July, 25, 2017
-# Purpose: Ros node to detect objects using tensorflow
-
-import os
-import sys
-import cv2
-import numpy as np
-try:
-    import tensorflow as tf
-except ImportError:
-    print("unable to import TensorFlow. Is it installed?")
-    print("  sudo apt install python-pip")
-    print("  sudo pip install tensorflow")
-    sys.exit(1)
-
-# ROS related imports
-import rospy
-from std_msgs.msg import String , Header
-from sensor_msgs.msg import Image
-from cv_bridge import CvBridge, CvBridgeError
-from vision_msgs.msg import Detection2D, Detection2DArray, ObjectHypothesisWithPose
-
-# Object detection module imports
-import object_detection
-from object_detection.utils import label_map_util
-from object_detection.utils import visualization_utils as vis_util
-
-# SET FRACTION OF GPU YOU WANT TO USE HERE
-GPU_FRACTION = 0.4
-
-######### Set model here ############
-MODEL_NAME =  'ssd_mobilenet_v1_coco_11_06_2017'
-# By default models are stored in data/models/
-MODEL_PATH = os.path.join(os.path.dirname(sys.path[0]),'data','models' , MODEL_NAME)
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
-PATH_TO_CKPT = MODEL_PATH + '/frozen_inference_graph.pb'
-######### Set the label map file here ###########
-LABEL_NAME = 'mscoco_label_map.pbtxt'
-# By default label maps are stored in data/labels/
-PATH_TO_LABELS = os.path.join(os.path.dirname(sys.path[0]),'data','labels', LABEL_NAME)
-######### Set the number of classes here #########
-NUM_CLASSES = 90
-
-detection_graph = tf.Graph()
-with detection_graph.as_default():
-    od_graph_def = tf.GraphDef()
-    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
-        serialized_graph = fid.read()
-        od_graph_def.ParseFromString(serialized_graph)
-        tf.import_graph_def(od_graph_def, name='')
-
-## Loading label map
-# Label maps map indices to category names, so that when our convolution network predicts `5`,
-# we know that this corresponds to `airplane`.  Here we use internal utility functions,
-# but anything that returns a dictionary mapping integers to appropriate string labels would be fine
-label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
-categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
-category_index = label_map_util.create_category_index(categories)
-
-# Setting the GPU options to use fraction of gpu that has been set
-config = tf.ConfigProto()
-config.gpu_options.per_process_gpu_memory_fraction = GPU_FRACTION
-
-# Detection
-
-class Detector:
-
-    def __init__(self):
-        self.image_pub = rospy.Publisher("debug_image",Image, queue_size=1)
-        self.object_pub = rospy.Publisher("objects", Detection2DArray, queue_size=1)
-        self.bridge = CvBridge()
-        self.image_sub = rospy.Subscriber("image", Image, self.image_cb, queue_size=1, buff_size=2**24)
-        self.sess = tf.Session(graph=detection_graph,config=config)
-
-    def image_cb(self, data):
-        objArray = Detection2DArray()
-        try:
-            cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
-        except CvBridgeError as e:
-            print(e)
-        image=cv2.cvtColor(cv_image,cv2.COLOR_BGR2RGB)
-
-        # the array based representation of the image will be used later in order to prepare the
-        # result image with boxes and labels on it.
-        image_np = np.asarray(image)
-        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
-        image_np_expanded = np.expand_dims(image_np, axis=0)
-        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
-        # Each box represents a part of the image where a particular object was detected.
-        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
-        # Each score represent how level of confidence for each of the objects.
-        # Score is shown on the result image, together with the class label.
-        scores = detection_graph.get_tensor_by_name('detection_scores:0')
-        classes = detection_graph.get_tensor_by_name('detection_classes:0')
-        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
-
-        (boxes, scores, classes, num_detections) = self.sess.run([boxes, scores, classes, num_detections],
-            feed_dict={image_tensor: image_np_expanded})
-
-        objects=vis_util.visualize_boxes_and_labels_on_image_array(
-            image,
-            np.squeeze(boxes),
-            np.squeeze(classes).astype(np.int32),
-            np.squeeze(scores),
-            category_index,
-            use_normalized_coordinates=True,
-            line_thickness=2)
-
-        objArray.detections =[]
-        objArray.header=data.header
-        object_count=1
-
-        for i in range(len(objects)):
-            object_count+=1
-            objArray.detections.append(self.object_predict(objects[i],data.header,image_np,cv_image))
-
-        self.object_pub.publish(objArray)
-
-        img=cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
-        image_out = Image()
-        try:
-            image_out = self.bridge.cv2_to_imgmsg(img,"bgr8")
-        except CvBridgeError as e:
-            print(e)
-        image_out.header = data.header
-        self.image_pub.publish(image_out)
-
-    def object_predict(self,object_data, header, image_np,image):
-        image_height,image_width,channels = image.shape
-        obj=Detection2D()
-        obj_hypothesis= ObjectHypothesisWithPose()
-
-        object_id=object_data[0]
-        object_score=object_data[1]
-        dimensions=object_data[2]
-
-        obj.header=header
-        obj_hypothesis.id = object_id
-        obj_hypothesis.score = object_score
-        obj.results.append(obj_hypothesis)
-        obj.bbox.size_y = int((dimensions[2]-dimensions[0])*image_height)
-        obj.bbox.size_x = int((dimensions[3]-dimensions[1] )*image_width)
-        obj.bbox.center.x = int((dimensions[1] + dimensions [3])*image_height/2)
-        obj.bbox.center.y = int((dimensions[0] + dimensions[2])*image_width/2)
-
-        return obj
-
-def main(args):
-    rospy.init_node('detector_node')
-    obj=Detector()
-    try:
-        rospy.spin()
-    except KeyboardInterrupt:
-        print("ShutDown")
-    cv2.destroyAllWindows()
-
-if __name__=='__main__':
-    main(sys.argv)
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rename from src/object_detection/protos/losses_pb2.py
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diff --git a/src/object_detection/protos/matcher.proto b/train/src/object_detection/protos/matcher.proto
similarity index 100%
rename from src/object_detection/protos/matcher.proto
rename to train/src/object_detection/protos/matcher.proto
diff --git a/src/object_detection/protos/matcher_pb2.py b/train/src/object_detection/protos/matcher_pb2.py
similarity index 100%
rename from src/object_detection/protos/matcher_pb2.py
rename to train/src/object_detection/protos/matcher_pb2.py
diff --git a/src/object_detection/protos/mean_stddev_box_coder.proto b/train/src/object_detection/protos/mean_stddev_box_coder.proto
similarity index 100%
rename from src/object_detection/protos/mean_stddev_box_coder.proto
rename to train/src/object_detection/protos/mean_stddev_box_coder.proto
diff --git a/src/object_detection/protos/mean_stddev_box_coder_pb2.py b/train/src/object_detection/protos/mean_stddev_box_coder_pb2.py
similarity index 100%
rename from src/object_detection/protos/mean_stddev_box_coder_pb2.py
rename to train/src/object_detection/protos/mean_stddev_box_coder_pb2.py
diff --git a/src/object_detection/protos/model.proto b/train/src/object_detection/protos/model.proto
similarity index 100%
rename from src/object_detection/protos/model.proto
rename to train/src/object_detection/protos/model.proto
diff --git a/src/object_detection/protos/model_pb2.py b/train/src/object_detection/protos/model_pb2.py
similarity index 100%
rename from src/object_detection/protos/model_pb2.py
rename to train/src/object_detection/protos/model_pb2.py
diff --git a/src/object_detection/protos/model_pb2.pyc b/train/src/object_detection/protos/model_pb2.pyc
similarity index 100%
rename from src/object_detection/protos/model_pb2.pyc
rename to train/src/object_detection/protos/model_pb2.pyc
diff --git a/src/object_detection/protos/optimizer.proto b/train/src/object_detection/protos/optimizer.proto
similarity index 100%
rename from src/object_detection/protos/optimizer.proto
rename to train/src/object_detection/protos/optimizer.proto
diff --git a/src/object_detection/protos/optimizer_pb2.py b/train/src/object_detection/protos/optimizer_pb2.py
similarity index 100%
rename from src/object_detection/protos/optimizer_pb2.py
rename to train/src/object_detection/protos/optimizer_pb2.py
diff --git a/src/object_detection/protos/pipeline.proto b/train/src/object_detection/protos/pipeline.proto
similarity index 100%
rename from src/object_detection/protos/pipeline.proto
rename to train/src/object_detection/protos/pipeline.proto
diff --git a/src/object_detection/protos/pipeline_pb2.py b/train/src/object_detection/protos/pipeline_pb2.py
similarity index 100%
rename from src/object_detection/protos/pipeline_pb2.py
rename to train/src/object_detection/protos/pipeline_pb2.py
diff --git a/src/object_detection/protos/post_processing.proto b/train/src/object_detection/protos/post_processing.proto
similarity index 100%
rename from src/object_detection/protos/post_processing.proto
rename to train/src/object_detection/protos/post_processing.proto
diff --git a/src/object_detection/protos/post_processing_pb2.py b/train/src/object_detection/protos/post_processing_pb2.py
similarity index 100%
rename from src/object_detection/protos/post_processing_pb2.py
rename to train/src/object_detection/protos/post_processing_pb2.py
diff --git a/src/object_detection/protos/preprocessor.proto b/train/src/object_detection/protos/preprocessor.proto
similarity index 100%
rename from src/object_detection/protos/preprocessor.proto
rename to train/src/object_detection/protos/preprocessor.proto
diff --git a/src/object_detection/protos/preprocessor_pb2.py b/train/src/object_detection/protos/preprocessor_pb2.py
similarity index 100%
rename from src/object_detection/protos/preprocessor_pb2.py
rename to train/src/object_detection/protos/preprocessor_pb2.py
diff --git a/src/object_detection/protos/region_similarity_calculator.proto b/train/src/object_detection/protos/region_similarity_calculator.proto
similarity index 100%
rename from src/object_detection/protos/region_similarity_calculator.proto
rename to train/src/object_detection/protos/region_similarity_calculator.proto
diff --git a/src/object_detection/protos/region_similarity_calculator_pb2.py b/train/src/object_detection/protos/region_similarity_calculator_pb2.py
similarity index 100%
rename from src/object_detection/protos/region_similarity_calculator_pb2.py
rename to train/src/object_detection/protos/region_similarity_calculator_pb2.py
diff --git a/src/object_detection/protos/square_box_coder.proto b/train/src/object_detection/protos/square_box_coder.proto
similarity index 100%
rename from src/object_detection/protos/square_box_coder.proto
rename to train/src/object_detection/protos/square_box_coder.proto
diff --git a/src/object_detection/protos/square_box_coder_pb2.py b/train/src/object_detection/protos/square_box_coder_pb2.py
similarity index 100%
rename from src/object_detection/protos/square_box_coder_pb2.py
rename to train/src/object_detection/protos/square_box_coder_pb2.py
diff --git a/src/object_detection/protos/ssd.proto b/train/src/object_detection/protos/ssd.proto
similarity index 100%
rename from src/object_detection/protos/ssd.proto
rename to train/src/object_detection/protos/ssd.proto
diff --git a/src/object_detection/protos/ssd_anchor_generator.proto b/train/src/object_detection/protos/ssd_anchor_generator.proto
similarity index 100%
rename from src/object_detection/protos/ssd_anchor_generator.proto
rename to train/src/object_detection/protos/ssd_anchor_generator.proto
diff --git a/src/object_detection/protos/ssd_anchor_generator_pb2.py b/train/src/object_detection/protos/ssd_anchor_generator_pb2.py
similarity index 100%
rename from src/object_detection/protos/ssd_anchor_generator_pb2.py
rename to train/src/object_detection/protos/ssd_anchor_generator_pb2.py
diff --git a/src/object_detection/protos/ssd_pb2.py b/train/src/object_detection/protos/ssd_pb2.py
similarity index 100%
rename from src/object_detection/protos/ssd_pb2.py
rename to train/src/object_detection/protos/ssd_pb2.py
diff --git a/train/src/object_detection/protos/string_int_label_map.proto b/train/src/object_detection/protos/string_int_label_map.proto
new file mode 100644
index 0000000..0894183
--- /dev/null
+++ b/train/src/object_detection/protos/string_int_label_map.proto
@@ -0,0 +1,24 @@
+// Message to store the mapping from class label strings to class id. Datasets
+// use string labels to represent classes while the object detection framework
+// works with class ids. This message maps them so they can be converted back
+// and forth as needed.
+syntax = "proto2";
+
+package object_detection.protos;
+
+message StringIntLabelMapItem {
+  // String name. The most common practice is to set this to a MID or synsets
+  // id.
+  optional string name = 1;
+
+  // Integer id that maps to the string name above. Label ids should start from
+  // 1.
+  optional int32 id = 2;
+
+  // Human readable string label.
+  optional string display_name = 3;
+};
+
+message StringIntLabelMap {
+  repeated StringIntLabelMapItem item = 1;
+};
diff --git a/train/src/object_detection/protos/string_int_label_map_pb2.py b/train/src/object_detection/protos/string_int_label_map_pb2.py
new file mode 100644
index 0000000..1556736
--- /dev/null
+++ b/train/src/object_detection/protos/string_int_label_map_pb2.py
@@ -0,0 +1,123 @@
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: object_detection/protos/string_int_label_map.proto
+
+import sys
+_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf import descriptor_pb2
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor.FileDescriptor(
+  name='object_detection/protos/string_int_label_map.proto',
+  package='object_detection.protos',
+  syntax='proto2',
+  serialized_pb=_b('\n2object_detection/protos/string_int_label_map.proto\x12\x17object_detection.protos\"G\n\x15StringIntLabelMapItem\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\x05\x12\x14\n\x0c\x64isplay_name\x18\x03 \x01(\t\"Q\n\x11StringIntLabelMap\x12<\n\x04item\x18\x01 \x03(\x0b\x32..object_detection.protos.StringIntLabelMapItem')
+)
+
+
+
+
+_STRINGINTLABELMAPITEM = _descriptor.Descriptor(
+  name='StringIntLabelMapItem',
+  full_name='object_detection.protos.StringIntLabelMapItem',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='name', full_name='object_detection.protos.StringIntLabelMapItem.name', index=0,
+      number=1, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None),
+    _descriptor.FieldDescriptor(
+      name='id', full_name='object_detection.protos.StringIntLabelMapItem.id', index=1,
+      number=2, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None),
+    _descriptor.FieldDescriptor(
+      name='display_name', full_name='object_detection.protos.StringIntLabelMapItem.display_name', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=_b("").decode('utf-8'),
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto2',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=79,
+  serialized_end=150,
+)
+
+
+_STRINGINTLABELMAP = _descriptor.Descriptor(
+  name='StringIntLabelMap',
+  full_name='object_detection.protos.StringIntLabelMap',
+  filename=None,
+  file=DESCRIPTOR,
+  containing_type=None,
+  fields=[
+    _descriptor.FieldDescriptor(
+      name='item', full_name='object_detection.protos.StringIntLabelMap.item', index=0,
+      number=1, type=11, cpp_type=10, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      options=None),
+  ],
+  extensions=[
+  ],
+  nested_types=[],
+  enum_types=[
+  ],
+  options=None,
+  is_extendable=False,
+  syntax='proto2',
+  extension_ranges=[],
+  oneofs=[
+  ],
+  serialized_start=152,
+  serialized_end=233,
+)
+
+_STRINGINTLABELMAP.fields_by_name['item'].message_type = _STRINGINTLABELMAPITEM
+DESCRIPTOR.message_types_by_name['StringIntLabelMapItem'] = _STRINGINTLABELMAPITEM
+DESCRIPTOR.message_types_by_name['StringIntLabelMap'] = _STRINGINTLABELMAP
+_sym_db.RegisterFileDescriptor(DESCRIPTOR)
+
+StringIntLabelMapItem = _reflection.GeneratedProtocolMessageType('StringIntLabelMapItem', (_message.Message,), dict(
+  DESCRIPTOR = _STRINGINTLABELMAPITEM,
+  __module__ = 'object_detection.protos.string_int_label_map_pb2'
+  # @@protoc_insertion_point(class_scope:object_detection.protos.StringIntLabelMapItem)
+  ))
+_sym_db.RegisterMessage(StringIntLabelMapItem)
+
+StringIntLabelMap = _reflection.GeneratedProtocolMessageType('StringIntLabelMap', (_message.Message,), dict(
+  DESCRIPTOR = _STRINGINTLABELMAP,
+  __module__ = 'object_detection.protos.string_int_label_map_pb2'
+  # @@protoc_insertion_point(class_scope:object_detection.protos.StringIntLabelMap)
+  ))
+_sym_db.RegisterMessage(StringIntLabelMap)
+
+
+# @@protoc_insertion_point(module_scope)
diff --git a/src/object_detection/protos/train.proto b/train/src/object_detection/protos/train.proto
similarity index 100%
rename from src/object_detection/protos/train.proto
rename to train/src/object_detection/protos/train.proto
diff --git a/src/object_detection/protos/train_pb2.py b/train/src/object_detection/protos/train_pb2.py
similarity index 100%
rename from src/object_detection/protos/train_pb2.py
rename to train/src/object_detection/protos/train_pb2.py
diff --git a/src/object_detection/samples/cloud/cloud.yml b/train/src/object_detection/samples/cloud/cloud.yml
similarity index 100%
rename from src/object_detection/samples/cloud/cloud.yml
rename to train/src/object_detection/samples/cloud/cloud.yml
diff --git a/src/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config b/train/src/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config
rename to train/src/object_detection/samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config
diff --git a/src/object_detection/samples/configs/faster_rcnn_resnet101_pets.config b/train/src/object_detection/samples/configs/faster_rcnn_resnet101_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/faster_rcnn_resnet101_pets.config
rename to train/src/object_detection/samples/configs/faster_rcnn_resnet101_pets.config
diff --git a/src/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config b/train/src/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config
similarity index 100%
rename from src/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config
rename to train/src/object_detection/samples/configs/faster_rcnn_resnet101_voc07.config
diff --git a/src/object_detection/samples/configs/faster_rcnn_resnet152_pets.config b/train/src/object_detection/samples/configs/faster_rcnn_resnet152_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/faster_rcnn_resnet152_pets.config
rename to train/src/object_detection/samples/configs/faster_rcnn_resnet152_pets.config
diff --git a/src/object_detection/samples/configs/faster_rcnn_resnet50_pets.config b/train/src/object_detection/samples/configs/faster_rcnn_resnet50_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/faster_rcnn_resnet50_pets.config
rename to train/src/object_detection/samples/configs/faster_rcnn_resnet50_pets.config
diff --git a/src/object_detection/samples/configs/rfcn_resnet101_pets.config b/train/src/object_detection/samples/configs/rfcn_resnet101_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/rfcn_resnet101_pets.config
rename to train/src/object_detection/samples/configs/rfcn_resnet101_pets.config
diff --git a/src/object_detection/samples/configs/ssd_inception_v2_pets.config b/train/src/object_detection/samples/configs/ssd_inception_v2_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/ssd_inception_v2_pets.config
rename to train/src/object_detection/samples/configs/ssd_inception_v2_pets.config
diff --git a/src/object_detection/samples/configs/ssd_mobilenet_v1_pets.config b/train/src/object_detection/samples/configs/ssd_mobilenet_v1_pets.config
similarity index 100%
rename from src/object_detection/samples/configs/ssd_mobilenet_v1_pets.config
rename to train/src/object_detection/samples/configs/ssd_mobilenet_v1_pets.config
diff --git a/src/object_detection/test_images/Screenshot from 2017-07-06 16:36:56.png b/train/src/object_detection/test_images/Screenshot from 2017-07-06 16:36:56.png
similarity index 100%
rename from src/object_detection/test_images/Screenshot from 2017-07-06 16:36:56.png
rename to train/src/object_detection/test_images/Screenshot from 2017-07-06 16:36:56.png
diff --git a/src/object_detection/test_images/image1.jpg b/train/src/object_detection/test_images/image1.jpg
similarity index 100%
rename from src/object_detection/test_images/image1.jpg
rename to train/src/object_detection/test_images/image1.jpg
diff --git a/src/object_detection/test_images/image1.png b/train/src/object_detection/test_images/image1.png
similarity index 100%
rename from src/object_detection/test_images/image1.png
rename to train/src/object_detection/test_images/image1.png
diff --git a/src/object_detection/test_images/image2.jpg b/train/src/object_detection/test_images/image2.jpg
similarity index 100%
rename from src/object_detection/test_images/image2.jpg
rename to train/src/object_detection/test_images/image2.jpg
diff --git a/src/object_detection/test_images/image2.png b/train/src/object_detection/test_images/image2.png
similarity index 100%
rename from src/object_detection/test_images/image2.png
rename to train/src/object_detection/test_images/image2.png
diff --git a/src/object_detection/test_images/image3.jpg b/train/src/object_detection/test_images/image3.jpg
similarity index 100%
rename from src/object_detection/test_images/image3.jpg
rename to train/src/object_detection/test_images/image3.jpg
diff --git a/src/object_detection/test_images/image3.png b/train/src/object_detection/test_images/image3.png
similarity index 100%
rename from src/object_detection/test_images/image3.png
rename to train/src/object_detection/test_images/image3.png
diff --git a/src/object_detection/test_images/image4.jpg b/train/src/object_detection/test_images/image4.jpg
similarity index 100%
rename from src/object_detection/test_images/image4.jpg
rename to train/src/object_detection/test_images/image4.jpg
diff --git a/src/object_detection/test_images/image5.jpg b/train/src/object_detection/test_images/image5.jpg
similarity index 100%
rename from src/object_detection/test_images/image5.jpg
rename to train/src/object_detection/test_images/image5.jpg
diff --git a/src/object_detection/test_images/image55.jpg b/train/src/object_detection/test_images/image55.jpg
similarity index 100%
rename from src/object_detection/test_images/image55.jpg
rename to train/src/object_detection/test_images/image55.jpg
diff --git a/src/object_detection/test_images/image6.jpg b/train/src/object_detection/test_images/image6.jpg
similarity index 100%
rename from src/object_detection/test_images/image6.jpg
rename to train/src/object_detection/test_images/image6.jpg
diff --git a/src/object_detection/test_images/image_info.txt b/train/src/object_detection/test_images/image_info.txt
similarity index 100%
rename from src/object_detection/test_images/image_info.txt
rename to train/src/object_detection/test_images/image_info.txt
diff --git a/src/object_detection/testcamera.py b/train/src/object_detection/testcamera.py
similarity index 100%
rename from src/object_detection/testcamera.py
rename to train/src/object_detection/testcamera.py
diff --git a/src/object_detection/train.py b/train/src/object_detection/train.py
similarity index 100%
rename from src/object_detection/train.py
rename to train/src/object_detection/train.py
diff --git a/src/object_detection/trainer.py b/train/src/object_detection/trainer.py
similarity index 100%
rename from src/object_detection/trainer.py
rename to train/src/object_detection/trainer.py
diff --git a/src/object_detection/trainer_test.py b/train/src/object_detection/trainer_test.py
similarity index 100%
rename from src/object_detection/trainer_test.py
rename to train/src/object_detection/trainer_test.py
diff --git a/src/object_detection/utils/BUILD b/train/src/object_detection/utils/BUILD
similarity index 100%
rename from src/object_detection/utils/BUILD
rename to train/src/object_detection/utils/BUILD
diff --git a/train/src/object_detection/utils/__init__.py b/train/src/object_detection/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/object_detection/utils/category_util.py b/train/src/object_detection/utils/category_util.py
similarity index 100%
rename from src/object_detection/utils/category_util.py
rename to train/src/object_detection/utils/category_util.py
diff --git a/src/object_detection/utils/category_util_test.py b/train/src/object_detection/utils/category_util_test.py
similarity index 100%
rename from src/object_detection/utils/category_util_test.py
rename to train/src/object_detection/utils/category_util_test.py
diff --git a/src/object_detection/utils/dataset_util.py b/train/src/object_detection/utils/dataset_util.py
similarity index 100%
rename from src/object_detection/utils/dataset_util.py
rename to train/src/object_detection/utils/dataset_util.py
diff --git a/src/object_detection/utils/dataset_util_test.py b/train/src/object_detection/utils/dataset_util_test.py
similarity index 100%
rename from src/object_detection/utils/dataset_util_test.py
rename to train/src/object_detection/utils/dataset_util_test.py
diff --git a/train/src/object_detection/utils/label_map_util.py b/train/src/object_detection/utils/label_map_util.py
new file mode 100644
index 0000000..3b8857c
--- /dev/null
+++ b/train/src/object_detection/utils/label_map_util.py
@@ -0,0 +1,140 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+"""Label map utility functions."""
+
+import logging
+
+import tensorflow as tf
+from google.protobuf import text_format
+from object_detection.protos import string_int_label_map_pb2
+
+
+def _validate_label_map(label_map):
+  """Checks if a label map is valid.
+
+  Args:
+    label_map: StringIntLabelMap to validate.
+
+  Raises:
+    ValueError: if label map is invalid.
+  """
+  for item in label_map.item:
+    if item.id < 1:
+      raise ValueError('Label map ids should be >= 1.')
+
+
+def create_category_index(categories):
+  """Creates dictionary of COCO compatible categories keyed by category id.
+
+  Args:
+    categories: a list of dicts, each of which has the following keys:
+      'id': (required) an integer id uniquely identifying this category.
+      'name': (required) string representing category name
+        e.g., 'cat', 'dog', 'pizza'.
+
+  Returns:
+    category_index: a dict containing the same entries as categories, but keyed
+      by the 'id' field of each category.
+  """
+  category_index = {}
+  for cat in categories:
+    category_index[cat['id']] = cat
+  return category_index
+
+
+def convert_label_map_to_categories(label_map,
+                                    max_num_classes,
+                                    use_display_name=True):
+  """Loads label map proto and returns categories list compatible with eval.
+
+  This function loads a label map and returns a list of dicts, each of which
+  has the following keys:
+    'id': (required) an integer id uniquely identifying this category.
+    'name': (required) string representing category name
+      e.g., 'cat', 'dog', 'pizza'.
+  We only allow class into the list if its id-label_id_offset is
+  between 0 (inclusive) and max_num_classes (exclusive).
+  If there are several items mapping to the same id in the label map,
+  we will only keep the first one in the categories list.
+
+  Args:
+    label_map: a StringIntLabelMapProto or None.  If None, a default categories
+      list is created with max_num_classes categories.
+    max_num_classes: maximum number of (consecutive) label indices to include.
+    use_display_name: (boolean) choose whether to load 'display_name' field
+      as category name.  If False or if the display_name field does not exist,
+      uses 'name' field as category names instead.
+  Returns:
+    categories: a list of dictionaries representing all possible categories.
+  """
+  categories = []
+  list_of_ids_already_added = []
+  if not label_map:
+    label_id_offset = 1
+    for class_id in range(max_num_classes):
+      categories.append({
+          'id': class_id + label_id_offset,
+          'name': 'category_{}'.format(class_id + label_id_offset)
+      })
+    return categories
+  for item in label_map.item:
+    if not 0 < item.id <= max_num_classes:
+      logging.info('Ignore item %d since it falls outside of requested '
+                   'label range.', item.id)
+      continue
+    if use_display_name and item.HasField('display_name'):
+      name = item.display_name
+    else:
+      name = item.name
+    if item.id not in list_of_ids_already_added:
+      list_of_ids_already_added.append(item.id)
+      categories.append({'id': item.id, 'name': name})
+  return categories
+
+
+def load_labelmap(path):
+  """Loads label map proto.
+
+  Args:
+    path: path to StringIntLabelMap proto text file.
+  Returns:
+    a StringIntLabelMapProto
+  """
+  with tf.gfile.GFile(path, 'r') as fid:
+    label_map_string = fid.read()
+    label_map = string_int_label_map_pb2.StringIntLabelMap()
+    try:
+      text_format.Merge(label_map_string, label_map)
+    except text_format.ParseError:
+      label_map.ParseFromString(label_map_string)
+  _validate_label_map(label_map)
+  return label_map
+
+
+def get_label_map_dict(label_map_path):
+  """Reads a label map and returns a dictionary of label names to id.
+
+  Args:
+    label_map_path: path to label_map.
+
+  Returns:
+    A dictionary mapping label names to id.
+  """
+  label_map = load_labelmap(label_map_path)
+  label_map_dict = {}
+  for item in label_map.item:
+    label_map_dict[item.name] = item.id
+  return label_map_dict
diff --git a/src/object_detection/utils/label_map_util_test.py b/train/src/object_detection/utils/label_map_util_test.py
similarity index 100%
rename from src/object_detection/utils/label_map_util_test.py
rename to train/src/object_detection/utils/label_map_util_test.py
diff --git a/src/object_detection/utils/learning_schedules.py b/train/src/object_detection/utils/learning_schedules.py
similarity index 100%
rename from src/object_detection/utils/learning_schedules.py
rename to train/src/object_detection/utils/learning_schedules.py
diff --git a/src/object_detection/utils/learning_schedules_test.py b/train/src/object_detection/utils/learning_schedules_test.py
similarity index 100%
rename from src/object_detection/utils/learning_schedules_test.py
rename to train/src/object_detection/utils/learning_schedules_test.py
diff --git a/src/object_detection/utils/metrics.py b/train/src/object_detection/utils/metrics.py
similarity index 100%
rename from src/object_detection/utils/metrics.py
rename to train/src/object_detection/utils/metrics.py
diff --git a/src/object_detection/utils/metrics_test.py b/train/src/object_detection/utils/metrics_test.py
similarity index 100%
rename from src/object_detection/utils/metrics_test.py
rename to train/src/object_detection/utils/metrics_test.py
diff --git a/src/object_detection/utils/np_box_list.py b/train/src/object_detection/utils/np_box_list.py
similarity index 100%
rename from src/object_detection/utils/np_box_list.py
rename to train/src/object_detection/utils/np_box_list.py
diff --git a/src/object_detection/utils/np_box_list_ops.py b/train/src/object_detection/utils/np_box_list_ops.py
similarity index 100%
rename from src/object_detection/utils/np_box_list_ops.py
rename to train/src/object_detection/utils/np_box_list_ops.py
diff --git a/src/object_detection/utils/np_box_list_ops_test.py b/train/src/object_detection/utils/np_box_list_ops_test.py
similarity index 100%
rename from src/object_detection/utils/np_box_list_ops_test.py
rename to train/src/object_detection/utils/np_box_list_ops_test.py
diff --git a/src/object_detection/utils/np_box_list_test.py b/train/src/object_detection/utils/np_box_list_test.py
similarity index 100%
rename from src/object_detection/utils/np_box_list_test.py
rename to train/src/object_detection/utils/np_box_list_test.py
diff --git a/src/object_detection/utils/np_box_ops.py b/train/src/object_detection/utils/np_box_ops.py
similarity index 100%
rename from src/object_detection/utils/np_box_ops.py
rename to train/src/object_detection/utils/np_box_ops.py
diff --git a/src/object_detection/utils/np_box_ops_test.py b/train/src/object_detection/utils/np_box_ops_test.py
similarity index 100%
rename from src/object_detection/utils/np_box_ops_test.py
rename to train/src/object_detection/utils/np_box_ops_test.py
diff --git a/src/object_detection/utils/object_detection_evaluation.py b/train/src/object_detection/utils/object_detection_evaluation.py
similarity index 100%
rename from src/object_detection/utils/object_detection_evaluation.py
rename to train/src/object_detection/utils/object_detection_evaluation.py
diff --git a/src/object_detection/utils/object_detection_evaluation_test.py b/train/src/object_detection/utils/object_detection_evaluation_test.py
similarity index 100%
rename from src/object_detection/utils/object_detection_evaluation_test.py
rename to train/src/object_detection/utils/object_detection_evaluation_test.py
diff --git a/src/object_detection/utils/ops.py b/train/src/object_detection/utils/ops.py
similarity index 100%
rename from src/object_detection/utils/ops.py
rename to train/src/object_detection/utils/ops.py
diff --git a/src/object_detection/utils/ops_test.py b/train/src/object_detection/utils/ops_test.py
similarity index 100%
rename from src/object_detection/utils/ops_test.py
rename to train/src/object_detection/utils/ops_test.py
diff --git a/src/object_detection/utils/per_image_evaluation.py b/train/src/object_detection/utils/per_image_evaluation.py
similarity index 100%
rename from src/object_detection/utils/per_image_evaluation.py
rename to train/src/object_detection/utils/per_image_evaluation.py
diff --git a/src/object_detection/utils/per_image_evaluation_test.py b/train/src/object_detection/utils/per_image_evaluation_test.py
similarity index 100%
rename from src/object_detection/utils/per_image_evaluation_test.py
rename to train/src/object_detection/utils/per_image_evaluation_test.py
diff --git a/src/object_detection/utils/shape_utils.py b/train/src/object_detection/utils/shape_utils.py
similarity index 100%
rename from src/object_detection/utils/shape_utils.py
rename to train/src/object_detection/utils/shape_utils.py
diff --git a/src/object_detection/utils/shape_utils_test.py b/train/src/object_detection/utils/shape_utils_test.py
similarity index 100%
rename from src/object_detection/utils/shape_utils_test.py
rename to train/src/object_detection/utils/shape_utils_test.py
diff --git a/src/object_detection/utils/static_shape.py b/train/src/object_detection/utils/static_shape.py
similarity index 100%
rename from src/object_detection/utils/static_shape.py
rename to train/src/object_detection/utils/static_shape.py
diff --git a/src/object_detection/utils/static_shape_test.py b/train/src/object_detection/utils/static_shape_test.py
similarity index 100%
rename from src/object_detection/utils/static_shape_test.py
rename to train/src/object_detection/utils/static_shape_test.py
diff --git a/src/object_detection/utils/test_utils.py b/train/src/object_detection/utils/test_utils.py
similarity index 100%
rename from src/object_detection/utils/test_utils.py
rename to train/src/object_detection/utils/test_utils.py
diff --git a/src/object_detection/utils/test_utils_test.py b/train/src/object_detection/utils/test_utils_test.py
similarity index 100%
rename from src/object_detection/utils/test_utils_test.py
rename to train/src/object_detection/utils/test_utils_test.py
diff --git a/src/object_detection/utils/variables_helper.py b/train/src/object_detection/utils/variables_helper.py
similarity index 100%
rename from src/object_detection/utils/variables_helper.py
rename to train/src/object_detection/utils/variables_helper.py
diff --git a/src/object_detection/utils/variables_helper_test.py b/train/src/object_detection/utils/variables_helper_test.py
similarity index 100%
rename from src/object_detection/utils/variables_helper_test.py
rename to train/src/object_detection/utils/variables_helper_test.py
diff --git a/train/src/object_detection/utils/visualization_utils.py b/train/src/object_detection/utils/visualization_utils.py
new file mode 100644
index 0000000..3c2c4b1
--- /dev/null
+++ b/train/src/object_detection/utils/visualization_utils.py
@@ -0,0 +1,451 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+"""A set of functions that are used for visualization.
+
+These functions often receive an image, perform some visualization on the image.
+The functions do not return a value, instead they modify the image itself.
+
+"""
+import collections
+import numpy as np
+import PIL.Image as Image
+import PIL.ImageColor as ImageColor
+import PIL.ImageDraw as ImageDraw
+import PIL.ImageFont as ImageFont
+import six
+import tensorflow as tf
+
+
+_TITLE_LEFT_MARGIN = 10
+_TITLE_TOP_MARGIN = 10
+STANDARD_COLORS = [
+    'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
+    'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
+    'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan',
+    'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange',
+    'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet',
+    'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite',
+    'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod',
+    'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki',
+    'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue',
+    'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey',
+    'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue',
+    'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime',
+    'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid',
+    'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen',
+    'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin',
+    'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed',
+    'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed',
+    'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple',
+    'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown',
+    'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue',
+    'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow',
+    'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White',
+    'WhiteSmoke', 'Yellow', 'YellowGreen'
+]
+
+
+def save_image_array_as_png(image, output_path):
+  """Saves an image (represented as a numpy array) to PNG.
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+    output_path: path to which image should be written.
+  """
+  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
+  with tf.gfile.Open(output_path, 'w') as fid:
+    image_pil.save(fid, 'PNG')
+
+
+def encode_image_array_as_png_str(image):
+  """Encodes a numpy array into a PNG string.
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+
+  Returns:
+    PNG encoded image string.
+  """
+  image_pil = Image.fromarray(np.uint8(image))
+  output = six.BytesIO()
+  image_pil.save(output, format='PNG')
+  png_string = output.getvalue()
+  output.close()
+  return png_string
+
+
+def draw_bounding_box_on_image_array(image,
+                                     ymin,
+                                     xmin,
+                                     ymax,
+                                     xmax,
+                                     color='red',
+                                     thickness=4,
+                                     display_str_list=(),
+                                     use_normalized_coordinates=True):
+  """Adds a bounding box to an image (numpy array).
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+    ymin: ymin of bounding box in normalized coordinates (same below).
+    xmin: xmin of bounding box.
+    ymax: ymax of bounding box.
+    xmax: xmax of bounding box.
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list: list of strings to display in box
+                      (each to be shown on its own line).
+    use_normalized_coordinates: If True (default), treat coordinates
+      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
+      coordinates as absolute.
+  """
+  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
+  draw_bounding_box_on_image(image_pil, ymin, xmin, ymax, xmax, color,
+                             thickness, display_str_list,
+                             use_normalized_coordinates)
+  np.copyto(image, np.array(image_pil))
+
+
+def draw_bounding_box_on_image(image,
+                               ymin,
+                               xmin,
+                               ymax,
+                               xmax,
+                               color='red',
+                               thickness=4,
+                               display_str_list=(),
+                               use_normalized_coordinates=True):
+  """Adds a bounding box to an image.
+
+  Each string in display_str_list is displayed on a separate line above the
+  bounding box in black text on a rectangle filled with the input 'color'.
+
+  Args:
+    image: a PIL.Image object.
+    ymin: ymin of bounding box.
+    xmin: xmin of bounding box.
+    ymax: ymax of bounding box.
+    xmax: xmax of bounding box.
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list: list of strings to display in box
+                      (each to be shown on its own line).
+    use_normalized_coordinates: If True (default), treat coordinates
+      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
+      coordinates as absolute.
+  """
+  box=[xmin, xmax, ymin, ymax]
+  draw = ImageDraw.Draw(image)
+  im_width, im_height = image.size
+  # print(image.size)
+  if use_normalized_coordinates:
+    (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
+                                  ymin * im_height, ymax * im_height)
+
+    # print ("xmin :%f, xmax: %f, ymin :%f, ymax :%f " %(xmin,xmax,ymin,ymax))
+    # print ("left:%f, right: %f, top:%f, bottom :%f " %(left,right,top,bottom))
+    # print ("center :( %d , %d)" %((left+right)/2,(top+bottom)/2))
+
+  else:
+    (left, right, top, bottom) = (xmin, xmax, ymin, ymax)
+  draw.line([(left, top), (left, bottom), (right, bottom),
+             (right, top), (left, top)], width=thickness, fill=color)
+  try:
+    font = ImageFont.truetype('arial.ttf', 24)
+  except IOError:
+    font = ImageFont.load_default()
+
+  text_bottom = top
+  # Reverse list and print from bottom to top.
+  for display_str in display_str_list[::-1]:
+    text_width, text_height = font.getsize(display_str)
+    margin = np.ceil(0.05 * text_height)
+    draw.rectangle(
+        [(left, text_bottom - text_height - 2 * margin), (left + text_width,
+                                                          text_bottom)],
+        fill=color)
+    draw.text(
+        (left + margin, text_bottom - text_height - margin),
+        display_str,
+        fill='black',
+        font=font)
+    text_bottom -= text_height - 2 * margin
+    # display_name=display_str.split(":",1)
+    # class_name=str(display_name[0])
+    # score=int(display_name[1].split("%",1)[0])
+    # print("Class: %s , Score: %d" %(class_name,score))  
+
+
+def draw_bounding_boxes_on_image_array(image,
+                                       boxes,
+                                       color='red',
+                                       thickness=4,
+                                       display_str_list_list=()):
+  """Draws bounding boxes on image (numpy array).
+
+  Args:
+    image: a numpy array object.
+    boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
+           The coordinates are in normalized format between [0, 1].
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list_list: list of list of strings.
+                           a list of strings for each bounding box.
+                           The reason to pass a list of strings for a
+                           bounding box is that it might contain
+                           multiple labels.
+
+  Raises:
+    ValueError: if boxes is not a [N, 4] array
+  """
+  image_pil = Image.fromarray(image)
+  draw_bounding_boxes_on_image(image_pil, boxes, color, thickness,
+                               display_str_list_list)
+  np.copyto(image, np.array(image_pil))
+
+
+def draw_bounding_boxes_on_image(image,
+                                 boxes,
+                                 color='red',
+                                 thickness=4,
+                                 display_str_list_list=()):
+  """Draws bounding boxes on image.
+
+  Args:
+    image: a PIL.Image object.
+    boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).
+           The coordinates are in normalized format between [0, 1].
+    color: color to draw bounding box. Default is red.
+    thickness: line thickness. Default value is 4.
+    display_str_list_list: list of list of strings.
+                           a list of strings for each bounding box.
+                           The reason to pass a list of strings for a
+                           bounding box is that it might contain
+                           multiple labels.
+
+  Raises:
+    ValueError: if boxes is not a [N, 4] array
+  """
+  boxes_shape = boxes.shape
+  if not boxes_shape:
+    return
+  if len(boxes_shape) != 2 or boxes_shape[1] != 4:
+    raise ValueError('Input must be of size [N, 4]')
+  for i in range(boxes_shape[0]):
+    display_str_list = ()
+    if display_str_list_list:
+      display_str_list = display_str_list_list[i]
+    draw_bounding_box_on_image(image, boxes[i, 0], boxes[i, 1], boxes[i, 2],
+                               boxes[i, 3], color, thickness, display_str_list)
+
+
+def draw_keypoints_on_image_array(image,
+                                  keypoints,
+                                  color='red',
+                                  radius=2,
+                                  use_normalized_coordinates=True):
+  """Draws keypoints on an image (numpy array).
+
+  Args:
+    image: a numpy array with shape [height, width, 3].
+    keypoints: a numpy array with shape [num_keypoints, 2].
+    color: color to draw the keypoints with. Default is red.
+    radius: keypoint radius. Default value is 2.
+    use_normalized_coordinates: if True (default), treat keypoint values as
+      relative to the image.  Otherwise treat them as absolute.
+  """
+  image_pil = Image.fromarray(np.uint8(image)).convert('RGB')
+  draw_keypoints_on_image(image_pil, keypoints, color, radius,
+                          use_normalized_coordinates)
+  np.copyto(image, np.array(image_pil))
+
+
+def draw_keypoints_on_image(image,
+                            keypoints,
+                            color='red',
+                            radius=2,
+                            use_normalized_coordinates=True):
+  """Draws keypoints on an image.
+
+  Args:
+    image: a PIL.Image object.
+    keypoints: a numpy array with shape [num_keypoints, 2].
+    color: color to draw the keypoints with. Default is red.
+    radius: keypoint radius. Default value is 2.
+    use_normalized_coordinates: if True (default), treat keypoint values as
+      relative to the image.  Otherwise treat them as absolute.
+  """
+  draw = ImageDraw.Draw(image)
+  im_width, im_height = image.size
+  keypoints_x = [k[1] for k in keypoints]
+  keypoints_y = [k[0] for k in keypoints]
+  if use_normalized_coordinates:
+    keypoints_x = tuple([im_width * x for x in keypoints_x])
+    keypoints_y = tuple([im_height * y for y in keypoints_y])
+  for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y):
+    draw.ellipse([(keypoint_x - radius, keypoint_y - radius),
+                  (keypoint_x + radius, keypoint_y + radius)],
+                 outline=color, fill=color)
+
+
+def draw_mask_on_image_array(image, mask, color='red', alpha=0.7):
+  """Draws mask on an image.
+
+  Args:
+    image: uint8 numpy array with shape (img_height, img_height, 3)
+    mask: a float numpy array of shape (img_height, img_height) with
+      values between 0 and 1
+    color: color to draw the keypoints with. Default is red.
+    alpha: transparency value between 0 and 1. (default: 0.7)
+
+  Raises:
+    ValueError: On incorrect data type for image or masks.
+  """
+  if image.dtype != np.uint8:
+    raise ValueError('`image` not of type np.uint8')
+  if mask.dtype != np.float32:
+    raise ValueError('`mask` not of type np.float32')
+  if np.any(np.logical_or(mask > 1.0, mask < 0.0)):
+    raise ValueError('`mask` elements should be in [0, 1]')
+  rgb = ImageColor.getrgb(color)
+  pil_image = Image.fromarray(image)
+
+  solid_color = np.expand_dims(
+      np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3])
+  pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert('RGBA')
+  pil_mask = Image.fromarray(np.uint8(255.0*alpha*mask)).convert('L')
+  pil_image = Image.composite(pil_solid_color, pil_image, pil_mask)
+  np.copyto(image, np.array(pil_image.convert('RGB')))
+
+
+def visualize_boxes_and_labels_on_image_array(image,
+                                              boxes,
+                                              classes,
+                                              scores,
+                                              category_index,
+                                              instance_masks=None,
+                                              keypoints=None,
+                                              use_normalized_coordinates=False,
+                                              max_boxes_to_draw=20,
+                                              min_score_thresh=.5,
+                                              agnostic_mode=False,
+                                              line_thickness=4):
+  """Overlay labeled boxes on an image with formatted scores and label names.
+
+  This function groups boxes that correspond to the same location
+  and creates a display string for each detection and overlays these
+  on the image.  Note that this function modifies the image array in-place
+  and does not return anything.
+
+  Args:
+    image: uint8 numpy array with shape (img_height, img_width, 3)
+    boxes: a numpy array of shape [N, 4]
+    classes: a numpy array of shape [N]
+    scores: a numpy array of shape [N] or None.  If scores=None, then
+      this function assumes that the boxes to be plotted are groundtruth
+      boxes and plot all boxes as black with no classes or scores.
+    category_index: a dict containing category dictionaries (each holding
+      category index `id` and category name `name`) keyed by category indices.
+    instance_masks: a numpy array of shape [N, image_height, image_width], can
+      be None
+    keypoints: a numpy array of shape [N, num_keypoints, 2], can
+      be None
+    use_normalized_coordinates: whether boxes is to be interpreted as
+      normalized coordinates or not.
+    max_boxes_to_draw: maximum number of boxes to visualize.  If None, draw
+      all boxes.
+    min_score_thresh: minimum score threshold for a box to be visualized
+    agnostic_mode: boolean (default: False) controlling whether to evaluate in
+      class-agnostic mode or not.  This mode will display scores but ignore
+      classes.
+    line_thickness: integer (default: 4) controlling line width of the boxes.
+  """
+  # Create a display string (and color) for every box location, group any boxes
+  # that correspond to the same location.
+  # print(image.size)
+  box_to_display_str_map = collections.defaultdict(list)
+  box_to_ID_score=collections.defaultdict(list)
+  box_to_color_map = collections.defaultdict(str)
+  box_to_instance_masks_map = {}
+  box_to_keypoints_map = collections.defaultdict(list)
+  # a=0
+  data = []
+  if not max_boxes_to_draw:
+    max_boxes_to_draw = boxes.shape[0]
+  for i in range(min(max_boxes_to_draw, boxes.shape[0])):
+    if scores is None or scores[i] > min_score_thresh:
+      box = tuple(boxes[i].tolist())
+
+      if instance_masks is not None:
+        box_to_instance_masks_map[box] = instance_masks[i]
+      if keypoints is not None:
+        box_to_keypoints_map[box].extend(keypoints[i])
+      if scores is None:
+        box_to_color_map[box] = 'black'
+      else:
+        if not agnostic_mode:
+          if classes[i] in category_index.keys():
+            class_name = category_index[classes[i]]['name']
+            class_id = category_index[classes[i]]['id']
+            class_score = scores[i]
+          else:
+            class_name = 'N/A'
+          display_str = '{}: {}%'.format(
+              class_name,
+              int(100*scores[i]))
+        else:
+          display_str = 'score: {}%'.format(int(100 * scores[i]))
+        box_to_display_str_map[box].append(display_str)
+        # box_to_ID_score[box].append(class_id)
+        data.append([class_id,class_score,box,])
+
+        # print(box_to_ID_score[box])
+        if agnostic_mode:
+          box_to_color_map[box] = 'DarkOrange'
+        else:
+          box_to_color_map[box] = STANDARD_COLORS[
+              classes[i] % len(STANDARD_COLORS)]
+
+  for box, color in box_to_color_map.items():
+    ymin, xmin, ymax, xmax = box
+    if instance_masks is not None:
+      draw_mask_on_image_array(
+          image,
+          box_to_instance_masks_map[box],
+          color=color
+      )
+  
+    draw_bounding_box_on_image_array(
+        image,
+        ymin,
+        xmin,
+        ymax,
+        xmax,
+        color=color,
+        thickness=line_thickness,
+        display_str_list=box_to_display_str_map[box],
+        use_normalized_coordinates=use_normalized_coordinates)
+    if keypoints is not None:
+      draw_keypoints_on_image_array(
+          image,
+          box_to_keypoints_map[box],
+          color=color,
+          radius=line_thickness / 2,
+          use_normalized_coordinates=use_normalized_coordinates)
+  # print(box_to_display_str_map.items())
+  # return (box_to_display_str_map.items())
+  return (data)
+
diff --git a/src/object_detection/utils/visualization_utils_test.py b/train/src/object_detection/utils/visualization_utils_test.py
similarity index 100%
rename from src/object_detection/utils/visualization_utils_test.py
rename to train/src/object_detection/utils/visualization_utils_test.py