From 99cbf4a6e1ce6ccbac3d06a5e60c521aac2676ff Mon Sep 17 00:00:00 2001
From: kkrampa <kamilkrampa87@gmail.com>
Date: Wed, 13 May 2020 00:19:04 +0200
Subject: [PATCH 01/16] Tweaks

---
 Dockerfile-x86-openvino                 | 4 +---
 download_openvino_model.sh              | 4 ++++
 {data => libs/detectors/x86/data}/.keep | 0
 libs/detectors/x86/openvino.py          | 4 ++--
 4 files changed, 7 insertions(+), 5 deletions(-)
 create mode 100755 download_openvino_model.sh
 rename {data => libs/detectors/x86/data}/.keep (100%)

diff --git a/Dockerfile-x86-openvino b/Dockerfile-x86-openvino
index bf033597..607cc01e 100644
--- a/Dockerfile-x86-openvino
+++ b/Dockerfile-x86-openvino
@@ -1,4 +1,4 @@
-FROM openvino/ubuntu18_dev
+FROM openvino/ubuntu18_runtime
 
 USER root
 VOLUME  /repo
@@ -8,7 +8,5 @@ RUN apt-get update && apt-get install -y pkg-config libsm6 libxext6 libxrender-d
 
 RUN pip3 install --upgrade pip setuptools==41.0.0 && pip3 install opencv-python wget flask scipy image
 
-RUN /opt/intel/openvino/deployment_tools/tools/model_downloader/downloader.py --name pedestrian-detection-adas-0002 --precisions FP32
-
 EXPOSE 8000
 
diff --git a/download_openvino_model.sh b/download_openvino_model.sh
new file mode 100755
index 00000000..71e8b992
--- /dev/null
+++ b/download_openvino_model.sh
@@ -0,0 +1,4 @@
+wget https://download.01.org/opencv/2020/openvinotoolkit/2020.2/open_model_zoo/models_bin/3/person-detection-retail-0013/FP32/person-detection-retail-0013.xml \
+  -O libs/detectors/x86/data/person-detection-retail-0013.xml
+wget https://download.01.org/opencv/2020/openvinotoolkit/2020.2/open_model_zoo/models_bin/3/person-detection-retail-0013/FP32/person-detection-retail-0013.bin \
+  -O libs/detectors/x86/data/person-detection-retail-0013.bin
diff --git a/data/.keep b/libs/detectors/x86/data/.keep
similarity index 100%
rename from data/.keep
rename to libs/detectors/x86/data/.keep
diff --git a/libs/detectors/x86/openvino.py b/libs/detectors/x86/openvino.py
index fc8f9e69..3b6f49c6 100644
--- a/libs/detectors/x86/openvino.py
+++ b/libs/detectors/x86/openvino.py
@@ -6,7 +6,7 @@
 
 from libs.detectors.utils.fps_calculator import convert_infr_time_to_fps
 
-from openvino.inference_engine import IENetwork, IECore
+from openvino.inference_engine import IECore
 
 class Detector:
     """
@@ -24,7 +24,7 @@ def __init__(self, config):
         # Frames Per Second
         self.fps = None
 
-        model_path = '/opt/intel/openvino_2020.2.120/deployment_tools/open_model_zoo/tools/downloader/intel/pedestrian-detection-adas-0002/FP32'
+        model_path = 'libs/detectors/x86/data'
 
         core = IECore()
         network = core.read_network(

From 81f5e5ecab93c15f22c8e2792df69a5014486b1e Mon Sep 17 00:00:00 2001
From: kkrampa <kamilkrampa87@gmail.com>
Date: Wed, 13 May 2020 00:30:29 +0200
Subject: [PATCH 02/16] Tweaks

---
 Dockerfile-x86-openvino |  2 ++
 README.md               | 14 ++++++++++++++
 config-x86-openvino.ini |  2 +-
 config-x86.ini          |  2 +-
 4 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/Dockerfile-x86-openvino b/Dockerfile-x86-openvino
index 607cc01e..aaaad6a4 100644
--- a/Dockerfile-x86-openvino
+++ b/Dockerfile-x86-openvino
@@ -10,3 +10,5 @@ RUN pip3 install --upgrade pip setuptools==41.0.0 && pip3 install opencv-python
 
 EXPOSE 8000
 
+CMD source /opt/intel/openvino/bin/setupvars.sh && python3 neuralet-distancing.py --config=config-x86-openvino.ini
+
diff --git a/README.md b/README.md
index 08155920..b7f4031b 100644
--- a/README.md
+++ b/README.md
@@ -99,6 +99,20 @@ docker build -f Dockerfile-x86 -t "neuralet/x86_64:applications-smart-distancing
 docker run -it -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/x86_64:applications-smart-distancing
 ```
 
+**Run on x86 using OpenVino**
+
+
+```
+cd neuralet/applications/smart-distancing/
+# download model first
+./download_openvino_model.sh
+
+# 1) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
+docker build -f Dockerfile-x86-openvino -t "neuralet/x86_64-openvino:applications-smart-distancing" .
+# 2) Run Docker container:
+docker run -it -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/x86_64-openvino:applications-smart-distancing
+```
+
 ### Configurations
 You can read and modify the configurations in `config-jetson.ini` file for Jetson Nano and `config-skeleton.ini` file for Coral.
 
diff --git a/config-x86-openvino.ini b/config-x86-openvino.ini
index 770792f7..cf93e967 100644
--- a/config-x86-openvino.ini
+++ b/config-x86-openvino.ini
@@ -1,5 +1,5 @@
 [App]
-VideoPath: data/TownCentreXVID.avi
+VideoPath: /repo/applications/smart-distancing/data/TownCentreXVID.avi
 Host: 0.0.0.0
 Port: 8000
 Resolution: 640,480
diff --git a/config-x86.ini b/config-x86.ini
index 92171c58..6d11ba3d 100644
--- a/config-x86.ini
+++ b/config-x86.ini
@@ -1,5 +1,5 @@
 [App]
-VideoPath: data/TownCentreXVID.avi
+VideoPath: /repo/applications/smart-distancing/data/TownCentreXVID.avi
 Host: 0.0.0.0
 Port: 8000
 Resolution: 640,480

From 36f5b6f619fbef1f2d27b42075c22eadb0dfc597 Mon Sep 17 00:00:00 2001
From: Mohammad H Sekhavat <sekhavat17@gmail.com>
Date: Wed, 13 May 2020 13:21:33 -0400
Subject: [PATCH 03/16] Implement Dummy Detector

Detects random bounding boxes on each frame.
Also fixes: #84
---
 libs/core.py                     |  5 ++---
 libs/detectors/dummy/__init__.py |  0
 libs/detectors/dummy/detector.py | 29 +++++++++++++++++++++++++++++
 3 files changed, 31 insertions(+), 3 deletions(-)
 create mode 100644 libs/detectors/dummy/__init__.py
 create mode 100644 libs/detectors/dummy/detector.py

diff --git a/libs/core.py b/libs/core.py
index d2421c2d..ea175df8 100755
--- a/libs/core.py
+++ b/libs/core.py
@@ -25,7 +25,8 @@ def __init__(self, config):
             from libs.detectors.edgetpu.detector import Detector
             self.detector = Detector(self.config)
         elif self.device == 'Dummy':
-            self.detector = None
+            from libs.detectors.dummy.detector import Detector
+            self.detector = Detector(self.config)
         elif self.device == 'x86':
             from libs.detectors.x86.detector import Detector
             self.detector = Detector(self.config)
@@ -48,8 +49,6 @@ def __process(self, cv_image):
         return object_list list of  dict for each obj,
         obj["bbox"] is normalized coordinations for [x0, y0, x1, y1] of box
         """
-        if self.device == 'Dummy':
-            return cv_image, [], None
 
         # Resize input image to resolution
         resolution = [int(i) for i in self.config.get_section_dict('App')['Resolution'].split(',')]
diff --git a/libs/detectors/dummy/__init__.py b/libs/detectors/dummy/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/libs/detectors/dummy/detector.py b/libs/detectors/dummy/detector.py
new file mode 100644
index 00000000..0fa6165a
--- /dev/null
+++ b/libs/detectors/dummy/detector.py
@@ -0,0 +1,29 @@
+import numpy as np
+import time
+
+class Detector:
+    """
+    Detects Random bounding boxes
+
+    Detector class is a high level class for detecting object using edgetpu devices.
+    When an instance of the Detector is created you can call inference method and feed your
+    input image in order to get the detection results.
+
+    :param config: Is a ConfigEngine instance which provides necessary parameters.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        self.name = self.config.get_section_dict('Detector')['Name']
+        self.class_id = self.config.get_section_dict('Detector')['ClassID']
+
+    def inference(self, resized_rgb_image):
+        self.fps = int(np.random.rand() * 10 + 10)
+        time.sleep(1.0 / self.fps)
+        bbox_transform = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0], [0, 1, 0, 1]]) * 0.5
+        class_id = self.class_id
+        return [{
+            'id': str(class_id)+"-"+str(i),
+            'bbox': (bbox_transform @ np.random.rand(4)).tolist(),
+            'cls': class_id
+        } for i in range(np.random.randint(5))]

From 839b8e98082c020c996adf3e844c52a966654611 Mon Sep 17 00:00:00 2001
From: Mohammad H Sekhavat <sekhavat17@gmail.com>
Date: Wed, 13 May 2020 13:44:59 -0400
Subject: [PATCH 04/16] Reduce fps for dummy detector

---
 libs/detectors/dummy/detector.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/libs/detectors/dummy/detector.py b/libs/detectors/dummy/detector.py
index 0fa6165a..04a32003 100644
--- a/libs/detectors/dummy/detector.py
+++ b/libs/detectors/dummy/detector.py
@@ -18,7 +18,7 @@ def __init__(self, config):
         self.class_id = self.config.get_section_dict('Detector')['ClassID']
 
     def inference(self, resized_rgb_image):
-        self.fps = int(np.random.rand() * 10 + 10)
+        self.fps = np.random.choice([0.5, 1, 2])
         time.sleep(1.0 / self.fps)
         bbox_transform = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0], [0, 1, 0, 1]]) * 0.5
         class_id = self.class_id

From 40d4bf25f95516c4b129479b4abc1dab452b3778 Mon Sep 17 00:00:00 2001
From: Mohsen Hejrati <mhejrati@gmail.com>
Date: Thu, 14 May 2020 21:30:30 -0700
Subject: [PATCH 05/16] add slack channel.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b7f4031b..fa56ab07 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ You can run this application on edge devices such as NVIDIA's Jetson Nano or Goo
 
 We encourage the community to join us in building a practical solution to keep people safe while allowing them to get back to their jobs. You can read more about the project motivation and roadmap here: https://docs.google.com/presentation/d/13EEt4JfdkYSqpPLpotx9taBHpNW6WtfXo2SfwFU_aQ0/edit?usp=sharing
 
-Please reach out to covid19project@neuralet.com if you have any questions. 
+Please join [our slack channel](https://neuralet.slack.com/join/shared_invite/zt-envn1kqo-PE5qB~yE~Y_t0kkUSI~HWw) or reach out to covid19project@neuralet.com if you have any questions. 
 
 
 ## Getting Started

From 0bfea43f352dd044b44aece5c2b0f220ae7e31a3 Mon Sep 17 00:00:00 2001
From: mrn-mln <mehraliangit@gmail.com>
Date: Sun, 17 May 2020 17:21:00 +0430
Subject: [PATCH 06/16] fix #100

---
 ui/utils/visualization_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ui/utils/visualization_utils.py b/ui/utils/visualization_utils.py
index 5e8f8c52..b1944c07 100644
--- a/ui/utils/visualization_utils.py
+++ b/ui/utils/visualization_utils.py
@@ -409,8 +409,8 @@ def visualization_preparation(nn_out, distances, dist_threshold):
     detection_boxes = []
     is_violating = []
     colors = []
-    
-    distance = np.amin(distances + np.identity(len(distances)) * dist_threshold * 2, 0) if distances !=[] else [0]
+
+    distance = np.amin(distances + np.identity(len(distances)) * dist_threshold * 2, 0) if distances != [] else [dist_threshold]
     for i, obj in enumerate(nn_out):
         # Colorizing bounding box based on the distances between them
         # R = 255 when dist=0 and R = 0 when dist > dist_threshold

From 932a580cd266a0c2e886cb62cd1c5ee7c25b9be6 Mon Sep 17 00:00:00 2001
From: Michael de Gans <47511965+mdegans@users.noreply.github.com>
Date: Mon, 18 May 2020 16:45:39 -0700
Subject: [PATCH 07/16] rename dockerfiles (#92)

* rename dockerfiles
- some IDEs like this better (eg. vscode)
---
 README.md                                              | 10 +++++-----
 Dockerfile-amd64-usbtpu => amd64-usbtpu.Dockerfile     |  0
 ...rfile-coral-dev-board => coral-dev-board.Dockerfile |  0
 Dockerfile-jetson-nano => jetson-nano.Dockerfile       |  0
 Dockerfile-x86-openvino => x86-openvino.Dockerfile     |  0
 Dockerfile-x86 => x86.Dockerfile                       |  0
 6 files changed, 5 insertions(+), 5 deletions(-)
 rename Dockerfile-amd64-usbtpu => amd64-usbtpu.Dockerfile (100%)
 rename Dockerfile-coral-dev-board => coral-dev-board.Dockerfile (100%)
 rename Dockerfile-jetson-nano => jetson-nano.Dockerfile (100%)
 rename Dockerfile-x86-openvino => x86-openvino.Dockerfile (100%)
 rename Dockerfile-x86 => x86.Dockerfile (100%)

diff --git a/README.md b/README.md
index fa56ab07..1b1bcea5 100644
--- a/README.md
+++ b/README.md
@@ -63,7 +63,7 @@ cd neuralet/applications/smart-distancing/
 ./download_jetson_trt.sh
 
 # 2) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
-docker build -f Dockerfile-jetson-nano -t "neuralet/jetson-nano:applications-smart-distancing" .
+docker build -f jetson-nano.Dockerfile -t "neuralet/jetson-nano:applications-smart-distancing" .
 
 # 3) Run Docker container:
 docker run -it --runtime nvidia --privileged -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/jetson-nano:applications-smart-distancing
@@ -74,7 +74,7 @@ docker run -it --runtime nvidia --privileged -p HOST_PORT:8000 -v /PATH_TO_CLONE
 cd neuralet/applications/smart-distancing/
 
 # 1) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
-docker build -f Dockerfile-coral-dev-board -t "neuralet/coral-dev-board:applications-smart-distancing" .
+docker build -f coral-dev-board.Dockerfile -t "neuralet/coral-dev-board:applications-smart-distancing" .
 # 2) Run Docker container:
 docker run -it --privileged -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/coral-dev-board:applications-smart-distancing
 ```
@@ -84,7 +84,7 @@ docker run -it --privileged -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/rep
 cd neuralet/applications/smart-distancing/
 
 # 1) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
-docker build -f Dockerfile-amd64-usbtpu -t "neuralet/amd64:applications-smart-distancing" .
+docker build -f amd64-usbtpu.Dockerfile -t "neuralet/amd64:applications-smart-distancing" .
 # 2) Run Docker container:
 docker run -it --privileged -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/amd64:applications-smart-distancing
 ```
@@ -94,7 +94,7 @@ docker run -it --privileged -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/rep
 cd neuralet/applications/smart-distancing/
 
 # 1) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
-docker build -f Dockerfile-x86 -t "neuralet/x86_64:applications-smart-distancing" .
+docker build -f x86.Dockerfile -t "neuralet/x86_64:applications-smart-distancing" .
 # 2) Run Docker container:
 docker run -it -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/x86_64:applications-smart-distancing
 ```
@@ -108,7 +108,7 @@ cd neuralet/applications/smart-distancing/
 ./download_openvino_model.sh
 
 # 1) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
-docker build -f Dockerfile-x86-openvino -t "neuralet/x86_64-openvino:applications-smart-distancing" .
+docker build -f x86-openvino.Dockerfile -t "neuralet/x86_64-openvino:applications-smart-distancing" .
 # 2) Run Docker container:
 docker run -it -p HOST_PORT:8000 -v /PATH_TO_CLONED_REPO_ROOT/:/repo neuralet/x86_64-openvino:applications-smart-distancing
 ```
diff --git a/Dockerfile-amd64-usbtpu b/amd64-usbtpu.Dockerfile
similarity index 100%
rename from Dockerfile-amd64-usbtpu
rename to amd64-usbtpu.Dockerfile
diff --git a/Dockerfile-coral-dev-board b/coral-dev-board.Dockerfile
similarity index 100%
rename from Dockerfile-coral-dev-board
rename to coral-dev-board.Dockerfile
diff --git a/Dockerfile-jetson-nano b/jetson-nano.Dockerfile
similarity index 100%
rename from Dockerfile-jetson-nano
rename to jetson-nano.Dockerfile
diff --git a/Dockerfile-x86-openvino b/x86-openvino.Dockerfile
similarity index 100%
rename from Dockerfile-x86-openvino
rename to x86-openvino.Dockerfile
diff --git a/Dockerfile-x86 b/x86.Dockerfile
similarity index 100%
rename from Dockerfile-x86
rename to x86.Dockerfile

From c3e550ca40472afc1acc748e0b4f1f597458286d Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 13:46:44 -0700
Subject: [PATCH 08/16] change load_model function in
 libs/detectors/x86/mobilenet_ssd.py

---
 libs/detectors/x86/mobilenet_ssd.py | 34 ++++++++++++++++-------------
 1 file changed, 19 insertions(+), 15 deletions(-)

diff --git a/libs/detectors/x86/mobilenet_ssd.py b/libs/detectors/x86/mobilenet_ssd.py
index c68bd2fe..529e9c11 100644
--- a/libs/detectors/x86/mobilenet_ssd.py
+++ b/libs/detectors/x86/mobilenet_ssd.py
@@ -1,27 +1,31 @@
 import pathlib
 import time
-
+import os
 import numpy as np
-
+import tarfile
+import wget
 import tensorflow as tf
 
 from libs.detectors.utils.fps_calculator import convert_infr_time_to_fps
 
 
 def load_model(model_name):
-  base_url = 'http://download.tensorflow.org/models/object_detection/'
-  model_file = model_name + '.tar.gz'
-  model_dir = tf.keras.utils.get_file(
-    fname=model_name,
-    origin=base_url + model_file,
-    untar=True)
-
-  model_dir = pathlib.Path(model_dir) / "saved_model"
-
-  model = tf.saved_model.load(str(model_dir))
-  model = model.signatures['serving_default']
-
-  return model
+    base_url = 'http://download.tensorflow.org/models/object_detection/'
+    model_file = model_name + '.tar.gz'
+    base_dir = "libs/detectors/x86/data/"
+    model_dir = os.path.join(base_dir, model_name)
+    if not os.path.isdir(model_dir):
+        print('model does not exist under: ', model_dir, 'downloading from ', base_url + model_file)
+        wget.download(base_url + model_file, base_dir)
+        with tarfile.open(base_dir + model_file, "r") as tar:
+            tar.extractall(path=base_dir)
+
+    model_dir = pathlib.Path(model_dir) / "saved_model"
+
+    model = tf.saved_model.load(str(model_dir))
+    model = model.signatures['serving_default']
+
+    return model
 
 
 class Detector:

From 4d493bbec1911933f9c96dfbd00bfa5afd1fce06 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 14:46:47 -0700
Subject: [PATCH 09/16] add pedestrian mobilenet v2 detector to the smart
 distancing

---
 config-x86.ini                                |  2 +-
 libs/detectors/x86/detector.py                |  3 +
 .../x86/pedestrian_ssd_mobilenet_v2.py        | 78 +++++++++++++++++++
 3 files changed, 82 insertions(+), 1 deletion(-)
 create mode 100644 libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py

diff --git a/config-x86.ini b/config-x86.ini
index 6d11ba3d..062a9c54 100644
--- a/config-x86.ini
+++ b/config-x86.ini
@@ -7,7 +7,7 @@ Resolution: 640,480
 [Detector]
 ; Supported devices: Jetson , EdgeTPU, Dummy
 Device: x86
-Name: mobilenet_ssd_v2
+Name: pedestrian_ssd_mobilenet_v2
 ;ImageSize should be 3 numbers seperated by commas, no spaces: 300,300,3
 ImageSize: 300,300,3
 ModelPath: 
diff --git a/libs/detectors/x86/detector.py b/libs/detectors/x86/detector.py
index 389c00f5..c5801c58 100644
--- a/libs/detectors/x86/detector.py
+++ b/libs/detectors/x86/detector.py
@@ -19,6 +19,9 @@ def __init__(self, config):
         elif self.name == "openvino":
             from libs.detectors.x86 import openvino
             self.net = openvino.Detector(self.config)
+        elif self.name == "pedestrian_ssd_mobilenet_v2":
+            from libs.detectors.x86 import pedestrian_ssd_mobilenet_v2
+            self.net = pedestrian_ssd_mobilenet_v2.Detector(self.config)
         else:
             raise ValueError('Not supported network named: ', self.name)
 
diff --git a/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py b/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
new file mode 100644
index 00000000..1be51f90
--- /dev/null
+++ b/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
@@ -0,0 +1,78 @@
+import pathlib
+import time
+import os
+import numpy as np
+import tarfile
+import wget
+import tensorflow as tf
+
+from libs.detectors.utils.fps_calculator import convert_infr_time_to_fps
+
+
+def load_model(model_name):
+    base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
+    model_file = model_name + "/saved_model/saved_model.pb"
+    base_dir = "libs/detectors/x86/data/"
+    model_dir = os.path.join(base_dir, model_name)
+    if not os.path.isdir(model_dir):
+        os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
+        print('model does not exist under: ', model_dir, 'downloading from ', base_url + model_file)
+        wget.download(base_url + model_file, os.path.join(model_dir, "saved_model"))
+
+    model_dir = pathlib.Path(model_dir) / "saved_model"
+
+    model = tf.saved_model.load(str(model_dir))
+    model = model.signatures['serving_default']
+
+    return model
+
+
+class Detector:
+    """
+    Perform object detection with the given model. The model is a quantized tflite
+    file which if the detector can not find it at the path it will download it
+    from neuralet repository automatically.
+
+    :param config: Is a ConfigEngine instance which provides necessary parameters.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        # Get the model name from the config
+        self.model_name = self.config.get_section_dict('Detector')['Name']
+        # Frames Per Second
+        self.fps = None
+
+        self.detection_model = load_model('ped_ssd_mobilenet_v2')
+
+    def inference(self, resized_rgb_image):
+        """
+        inference function sets input tensor to input image and gets the output.
+        The interpreter instance provides corresponding detection output which is used for creating result
+        Args:
+            resized_rgb_image: uint8 numpy array with shape (img_height, img_width, channels)
+
+        Returns:
+            result: a dictionary contains of [{"id": 0, "bbox": [x1, y1, x2, y2], "score":s%}, {...}, {...}, ...]
+        """
+        input_image = np.expand_dims(resized_rgb_image, axis=0)
+        input_tensor = tf.convert_to_tensor(input_image)
+        t_begin = time.perf_counter()
+        output_dict = self.detection_model(input_tensor)
+        inference_time = time.perf_counter() - t_begin  # Seconds
+
+        # Calculate Frames rate (fps)
+        self.fps = convert_infr_time_to_fps(inference_time)
+
+        boxes = output_dict['detection_boxes']
+        labels = output_dict['detection_classes']
+        scores = output_dict['detection_scores']
+
+        class_id = int(self.config.get_section_dict('Detector')['ClassID'])
+        score_threshold = float(self.config.get_section_dict('Detector')['MinScore'])
+        result = []
+        for i in range(boxes.shape[1]):  # number of boxes
+            if labels[0, i] == class_id and scores[0, i] > score_threshold:
+                result.append({"id": str(class_id) + '-' + str(i), "bbox": boxes[0, i, :], "score": scores[0, i]})
+
+        return result

From ea417510875118f5d12aa50e4c70c86c5f5aeae0 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 14:55:36 -0700
Subject: [PATCH 10/16] add pedestrian ssdlite mobilenet v2 detector to the
 smart distancing

---
 libs/detectors/x86/detector.py                |  3 +
 .../x86/pedestrian_ssdlite_mobilenet_v2.py    | 78 +++++++++++++++++++
 2 files changed, 81 insertions(+)
 create mode 100644 libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py

diff --git a/libs/detectors/x86/detector.py b/libs/detectors/x86/detector.py
index c5801c58..71bd9175 100644
--- a/libs/detectors/x86/detector.py
+++ b/libs/detectors/x86/detector.py
@@ -22,6 +22,9 @@ def __init__(self, config):
         elif self.name == "pedestrian_ssd_mobilenet_v2":
             from libs.detectors.x86 import pedestrian_ssd_mobilenet_v2
             self.net = pedestrian_ssd_mobilenet_v2.Detector(self.config)
+        elif self.name == "pedestrian_ssdlite_mobilenet_v2":
+            from libs.detectors.x86 import pedestrian_ssdlite_mobilenet_v2
+            self.net = pedestrian_ssdlite_mobilenet_v2.Detector(self.config)
         else:
             raise ValueError('Not supported network named: ', self.name)
 
diff --git a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
new file mode 100644
index 00000000..55b87995
--- /dev/null
+++ b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
@@ -0,0 +1,78 @@
+import pathlib
+import time
+import os
+import numpy as np
+import tarfile
+import wget
+import tensorflow as tf
+
+from libs.detectors.utils.fps_calculator import convert_infr_time_to_fps
+
+
+def load_model(model_name):
+    base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
+    model_file = model_name + "/saved_model/saved_model.pb"
+    base_dir = "libs/detectors/x86/data/"
+    model_dir = os.path.join(base_dir, model_name)
+    if not os.path.isdir(model_dir):
+        os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
+        print('model does not exist under: ', model_dir, 'downloading from ', base_url + model_file)
+        wget.download(base_url + model_file, os.path.join(model_dir, "saved_model"))
+
+    model_dir = pathlib.Path(model_dir) / "saved_model"
+
+    model = tf.saved_model.load(str(model_dir))
+    model = model.signatures['serving_default']
+
+    return model
+
+
+class Detector:
+    """
+    Perform object detection with the given model. The model is a quantized tflite
+    file which if the detector can not find it at the path it will download it
+    from neuralet repository automatically.
+
+    :param config: Is a ConfigEngine instance which provides necessary parameters.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        # Get the model name from the config
+        self.model_name = self.config.get_section_dict('Detector')['Name']
+        # Frames Per Second
+        self.fps = None
+
+        self.detection_model = load_model('ped_ssdlite_mobilenet_v2')
+
+    def inference(self, resized_rgb_image):
+        """
+        inference function sets input tensor to input image and gets the output.
+        The interpreter instance provides corresponding detection output which is used for creating result
+        Args:
+            resized_rgb_image: uint8 numpy array with shape (img_height, img_width, channels)
+
+        Returns:
+            result: a dictionary contains of [{"id": 0, "bbox": [x1, y1, x2, y2], "score":s%}, {...}, {...}, ...]
+        """
+        input_image = np.expand_dims(resized_rgb_image, axis=0)
+        input_tensor = tf.convert_to_tensor(input_image)
+        t_begin = time.perf_counter()
+        output_dict = self.detection_model(input_tensor)
+        inference_time = time.perf_counter() - t_begin  # Seconds
+
+        # Calculate Frames rate (fps)
+        self.fps = convert_infr_time_to_fps(inference_time)
+
+        boxes = output_dict['detection_boxes']
+        labels = output_dict['detection_classes']
+        scores = output_dict['detection_scores']
+
+        class_id = int(self.config.get_section_dict('Detector')['ClassID'])
+        score_threshold = float(self.config.get_section_dict('Detector')['MinScore'])
+        result = []
+        for i in range(boxes.shape[1]):  # number of boxes
+            if labels[0, i] == class_id and scores[0, i] > score_threshold:
+                result.append({"id": str(class_id) + '-' + str(i), "bbox": boxes[0, i, :], "score": scores[0, i]})
+
+        return result

From 408f2971ef9252a5e5e15de0ea9afe5540933241 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 15:03:37 -0700
Subject: [PATCH 11/16] add pedestrian ssdlite mobilenet v3 detector to the
 smart distancing

---
 libs/detectors/x86/detector.py                |  3 +
 .../x86/pedestrian_ssdlite_mobilenet_v3.py    | 78 +++++++++++++++++++
 2 files changed, 81 insertions(+)
 create mode 100644 libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py

diff --git a/libs/detectors/x86/detector.py b/libs/detectors/x86/detector.py
index 71bd9175..7f6bfab2 100644
--- a/libs/detectors/x86/detector.py
+++ b/libs/detectors/x86/detector.py
@@ -25,6 +25,9 @@ def __init__(self, config):
         elif self.name == "pedestrian_ssdlite_mobilenet_v2":
             from libs.detectors.x86 import pedestrian_ssdlite_mobilenet_v2
             self.net = pedestrian_ssdlite_mobilenet_v2.Detector(self.config)
+        elif self.name == "pedestrian_ssdlite_mobilenet_v3":
+            from libs.detectors.x86 import pedestrian_ssdlite_mobilenet_v3
+            self.net = pedestrian_ssdlite_mobilenet_v3.Detector(self.config)
         else:
             raise ValueError('Not supported network named: ', self.name)
 
diff --git a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
new file mode 100644
index 00000000..3b838cb7
--- /dev/null
+++ b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
@@ -0,0 +1,78 @@
+import pathlib
+import time
+import os
+import numpy as np
+import tarfile
+import wget
+import tensorflow as tf
+
+from libs.detectors.utils.fps_calculator import convert_infr_time_to_fps
+
+
+def load_model(model_name):
+    base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
+    model_file = model_name + "/saved_model/saved_model.pb"
+    base_dir = "libs/detectors/x86/data/"
+    model_dir = os.path.join(base_dir, model_name)
+    if not os.path.isdir(model_dir):
+        os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
+        print('model does not exist under: ', model_dir, 'downloading from ', base_url + model_file)
+        wget.download(base_url + model_file, os.path.join(model_dir, "saved_model"))
+
+    model_dir = pathlib.Path(model_dir) / "saved_model"
+
+    model = tf.saved_model.load(str(model_dir))
+    model = model.signatures['serving_default']
+
+    return model
+
+
+class Detector:
+    """
+    Perform object detection with the given model. The model is a quantized tflite
+    file which if the detector can not find it at the path it will download it
+    from neuralet repository automatically.
+
+    :param config: Is a ConfigEngine instance which provides necessary parameters.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        # Get the model name from the config
+        self.model_name = self.config.get_section_dict('Detector')['Name']
+        # Frames Per Second
+        self.fps = None
+
+        self.detection_model = load_model('ped_ssdlite_mobilenet_v3')
+
+    def inference(self, resized_rgb_image):
+        """
+        inference function sets input tensor to input image and gets the output.
+        The interpreter instance provides corresponding detection output which is used for creating result
+        Args:
+            resized_rgb_image: uint8 numpy array with shape (img_height, img_width, channels)
+
+        Returns:
+            result: a dictionary contains of [{"id": 0, "bbox": [x1, y1, x2, y2], "score":s%}, {...}, {...}, ...]
+        """
+        input_image = np.expand_dims(resized_rgb_image, axis=0)
+        input_tensor = tf.convert_to_tensor(input_image)
+        t_begin = time.perf_counter()
+        output_dict = self.detection_model(input_tensor)
+        inference_time = time.perf_counter() - t_begin  # Seconds
+
+        # Calculate Frames rate (fps)
+        self.fps = convert_infr_time_to_fps(inference_time)
+
+        boxes = output_dict['detection_boxes']
+        labels = output_dict['detection_classes']
+        scores = output_dict['detection_scores']
+
+        class_id = int(self.config.get_section_dict('Detector')['ClassID'])
+        score_threshold = float(self.config.get_section_dict('Detector')['MinScore'])
+        result = []
+        for i in range(boxes.shape[1]):  # number of boxes
+            if labels[0, i] == class_id and scores[0, i] > score_threshold:
+                result.append({"id": str(class_id) + '-' + str(i), "bbox": boxes[0, i, :], "score": scores[0, i]})
+
+        return result

From 606729a57f7780c3588d8411051b6793da0c2063 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 15:12:42 -0700
Subject: [PATCH 12/16] change COCO model from mobilenet_ssd to
 ssd_mobilenet_v2

---
 libs/detectors/x86/detector.py                            | 6 +++---
 libs/detectors/x86/{mobilenet_ssd.py => ssd_mobilenet_v2} | 0
 2 files changed, 3 insertions(+), 3 deletions(-)
 rename libs/detectors/x86/{mobilenet_ssd.py => ssd_mobilenet_v2} (100%)

diff --git a/libs/detectors/x86/detector.py b/libs/detectors/x86/detector.py
index 7f6bfab2..9b71e11f 100644
--- a/libs/detectors/x86/detector.py
+++ b/libs/detectors/x86/detector.py
@@ -13,9 +13,9 @@ def __init__(self, config):
         self.config = config
         self.name = self.config.get_section_dict('Detector')['Name']
 
-        if self.name == 'mobilenet_ssd_v2':
-            from libs.detectors.x86 import mobilenet_ssd
-            self.net = mobilenet_ssd.Detector(self.config)
+        if self.name == 'ssd_mobilenet_v2':
+            from libs.detectors.x86 import ssd_mobilenet_v2
+            self.net = ssd_mobilenet_v2.Detector(self.config)
         elif self.name == "openvino":
             from libs.detectors.x86 import openvino
             self.net = openvino.Detector(self.config)
diff --git a/libs/detectors/x86/mobilenet_ssd.py b/libs/detectors/x86/ssd_mobilenet_v2
similarity index 100%
rename from libs/detectors/x86/mobilenet_ssd.py
rename to libs/detectors/x86/ssd_mobilenet_v2

From 991749d56b7fc0b27042f9517261d47977fee0b8 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 15:13:24 -0700
Subject: [PATCH 13/16] modify config files

---
 config-skeleton.ini | 2 +-
 config-x86.ini      | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/config-skeleton.ini b/config-skeleton.ini
index 371b008a..d7770080 100644
--- a/config-skeleton.ini
+++ b/config-skeleton.ini
@@ -7,7 +7,7 @@ Resolution: 640,480
 [Detector]
 ; Supported devices: Jetson , EdgeTPU, Dummy
 Device: EdgeTPU
-; Detector's Name can be either "mobilenet_ssd_v2", "pedestrian_ssdlite_mobilenet_v2" or "pedestrian_ssdlite_mobilenet_v2"
+; Detector's Name can be either "mobilenet_ssd_v2", "pedestrian_ssd_mobilenet_v2" or "pedestrian_ssdlite_mobilenet_v2"
 ; the first one is trained on COCO dataset and next two are trained on Oxford Town Center dataset to detect pedestrians
 Name: pedestrian_ssdlite_mobilenet_v2
 ;ImageSize should be 3 numbers seperated by commas, no spaces: 300,300,3
diff --git a/config-x86.ini b/config-x86.ini
index 062a9c54..bfb9adad 100644
--- a/config-x86.ini
+++ b/config-x86.ini
@@ -7,8 +7,11 @@ Resolution: 640,480
 [Detector]
 ; Supported devices: Jetson , EdgeTPU, Dummy
 Device: x86
+;Name can be either ssd_mobilenet_v2 or pedestrian_ssd_mobilenet_v2 or pedestrian_ssdlite_mobilenet_v2 or pedestrian_ssdlite_mobilenet_v2
+;The first one is trained on COCO dataset and next three are trained on Oxford Town Center dataset to detect pedestrians
 Name: pedestrian_ssd_mobilenet_v2
 ;ImageSize should be 3 numbers seperated by commas, no spaces: 300,300,3
+;For the pedestrian_ssdlite_mobilenet_v3: 320,320,3
 ImageSize: 300,300,3
 ModelPath: 
 ClassID: 1

From d1bdca1c2ca41f7ada8c48ded8dc5c73ea58e732 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Mon, 25 May 2020 15:18:08 -0700
Subject: [PATCH 14/16] delete tarfile import

---
 libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py     | 1 -
 libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py | 1 -
 libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py | 1 -
 3 files changed, 3 deletions(-)

diff --git a/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py b/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
index 1be51f90..7c12aaef 100644
--- a/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
+++ b/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
@@ -2,7 +2,6 @@
 import time
 import os
 import numpy as np
-import tarfile
 import wget
 import tensorflow as tf
 
diff --git a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
index 55b87995..dc2b53e4 100644
--- a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
+++ b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
@@ -2,7 +2,6 @@
 import time
 import os
 import numpy as np
-import tarfile
 import wget
 import tensorflow as tf
 
diff --git a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
index 3b838cb7..30be361e 100644
--- a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
+++ b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
@@ -2,7 +2,6 @@
 import time
 import os
 import numpy as np
-import tarfile
 import wget
 import tensorflow as tf
 

From 99ac7857bfa2e3dbf0ed09183a1087b43f574468 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Wed, 3 Jun 2020 08:11:33 -0700
Subject: [PATCH 15/16] add pedestrian faster rcnn model for x86

---
 config-x86.ini                                |  4 +-
 libs/detectors/x86/detector.py                |  3 +
 .../x86/pedestrian_faster_rcnn_resnet50.py    | 77 +++++++++++++++++++
 3 files changed, 82 insertions(+), 2 deletions(-)
 create mode 100644 libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py

diff --git a/config-x86.ini b/config-x86.ini
index c1655d32..f0d22fb7 100644
--- a/config-x86.ini
+++ b/config-x86.ini
@@ -8,8 +8,8 @@ Resolution: 640,480
 [Detector]
 ; Supported devices: Jetson , EdgeTPU, Dummy
 Device: x86
-;Name can be either ssd_mobilenet_v2 or pedestrian_ssd_mobilenet_v2 or pedestrian_ssdlite_mobilenet_v2 or pedestrian_ssdlite_mobilenet_v2
-;The first one is trained on COCO dataset and next three are trained on Oxford Town Center dataset to detect pedestrians
+;Name can be either ssd_mobilenet_v2 or pedestrian_ssd_mobilenet_v2 or pedestrian_ssdlite_mobilenet_v2 or pedestrian_ssdlite_mobilenet_v2 or pedestrian_faster_rcnn_resnet50
+;The first one is trained on COCO dataset and next four are trained on Oxford Town Center dataset to detect pedestrians
 Name: pedestrian_ssd_mobilenet_v2
 ;ImageSize should be 3 numbers seperated by commas, no spaces: 300,300,3
 ;For the pedestrian_ssdlite_mobilenet_v3: 320,320,3
diff --git a/libs/detectors/x86/detector.py b/libs/detectors/x86/detector.py
index 9b71e11f..f4e9933c 100644
--- a/libs/detectors/x86/detector.py
+++ b/libs/detectors/x86/detector.py
@@ -28,6 +28,9 @@ def __init__(self, config):
         elif self.name == "pedestrian_ssdlite_mobilenet_v3":
             from libs.detectors.x86 import pedestrian_ssdlite_mobilenet_v3
             self.net = pedestrian_ssdlite_mobilenet_v3.Detector(self.config)
+        elif self.name == "pedestrian_faster_rcnn_resnet50":
+            from libs.detectors.x86 import pedestrian_faster_rcnn_resnet50
+            self.net = pedestrian_faster_rcnn_resnet50.Detector(self.config)
         else:
             raise ValueError('Not supported network named: ', self.name)
 
diff --git a/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py b/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py
new file mode 100644
index 00000000..49bcb75b
--- /dev/null
+++ b/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py
@@ -0,0 +1,77 @@
+import pathlib
+import time
+import os
+import numpy as np
+import wget
+import tensorflow as tf
+
+from libs.detectors.utils.fps_calculator import convert_infr_time_to_fps
+
+
+def load_model(model_name):
+    base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
+    model_file = model_name + "/saved_model/saved_model.pb"
+    base_dir = "libs/detectors/x86/data/"
+    model_dir = os.path.join(base_dir, model_name)
+    if not os.path.isdir(model_dir):
+        os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
+        print('model does not exist under: ', model_dir, 'downloading from ', base_url + model_file)
+        wget.download(base_url + model_file, os.path.join(model_dir, "saved_model"))
+
+    model_dir = pathlib.Path(model_dir) / "saved_model"
+
+    model = tf.saved_model.load(str(model_dir))
+    model = model.signatures['serving_default']
+
+    return model
+
+
+class Detector:
+    """
+    Perform object detection with the given model. The model is a quantized tflite
+    file which if the detector can not find it at the path it will download it
+    from neuralet repository automatically.
+
+    :param config: Is a ConfigEngine instance which provides necessary parameters.
+    """
+
+    def __init__(self, config):
+        self.config = config
+        # Get the model name from the config
+        self.model_name = self.config.get_section_dict('Detector')['Name']
+        # Frames Per Second
+        self.fps = None
+
+        self.detection_model = load_model('ped_faster_rcnn_resnet50')
+
+    def inference(self, resized_rgb_image):
+        """
+        inference function sets input tensor to input image and gets the output.
+        The interpreter instance provides corresponding detection output which is used for creating result
+        Args:
+            resized_rgb_image: uint8 numpy array with shape (img_height, img_width, channels)
+
+        Returns:
+            result: a dictionary contains of [{"id": 0, "bbox": [x1, y1, x2, y2], "score":s%}, {...}, {...}, ...]
+        """
+        input_image = np.expand_dims(resized_rgb_image, axis=0)
+        input_tensor = tf.convert_to_tensor(input_image)
+        t_begin = time.perf_counter()
+        output_dict = self.detection_model(input_tensor)
+        inference_time = time.perf_counter() - t_begin  # Seconds
+
+        # Calculate Frames rate (fps)
+        self.fps = convert_infr_time_to_fps(inference_time)
+
+        boxes = output_dict['detection_boxes']
+        labels = output_dict['detection_classes']
+        scores = output_dict['detection_scores']
+
+        class_id = int(self.config.get_section_dict('Detector')['ClassID'])
+        score_threshold = float(self.config.get_section_dict('Detector')['MinScore'])
+        result = []
+        for i in range(boxes.shape[1]):  # number of boxes
+            if labels[0, i] == class_id and scores[0, i] > score_threshold:
+                result.append({"id": str(class_id) + '-' + str(i), "bbox": boxes[0, i, :], "score": scores[0, i]})
+
+        return result

From b542a9718d6cbfe62897902ab586028b7485d1d9 Mon Sep 17 00:00:00 2001
From: alpha-carinae29 <alpha.carinae29@gmail.com>
Date: Wed, 3 Jun 2020 08:18:15 -0700
Subject: [PATCH 16/16] change model path

---
 libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py        | 2 +-
 libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py            | 2 +-
 libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py        | 2 +-
 libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py        | 2 +-
 libs/detectors/x86/{ssd_mobilenet_v2 => ssd_mobilenet_v2.py} | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)
 rename libs/detectors/x86/{ssd_mobilenet_v2 => ssd_mobilenet_v2.py} (98%)

diff --git a/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py b/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py
index 49bcb75b..1d4d8922 100644
--- a/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py
+++ b/libs/detectors/x86/pedestrian_faster_rcnn_resnet50.py
@@ -11,7 +11,7 @@
 def load_model(model_name):
     base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
     model_file = model_name + "/saved_model/saved_model.pb"
-    base_dir = "libs/detectors/x86/data/"
+    base_dir = "/repo/data/x86/"
     model_dir = os.path.join(base_dir, model_name)
     if not os.path.isdir(model_dir):
         os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
diff --git a/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py b/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
index 7c12aaef..772280d4 100644
--- a/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
+++ b/libs/detectors/x86/pedestrian_ssd_mobilenet_v2.py
@@ -11,7 +11,7 @@
 def load_model(model_name):
     base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
     model_file = model_name + "/saved_model/saved_model.pb"
-    base_dir = "libs/detectors/x86/data/"
+    base_dir = "/repo/data/x86/"
     model_dir = os.path.join(base_dir, model_name)
     if not os.path.isdir(model_dir):
         os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
diff --git a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
index dc2b53e4..84dd7977 100644
--- a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
+++ b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v2.py
@@ -11,7 +11,7 @@
 def load_model(model_name):
     base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
     model_file = model_name + "/saved_model/saved_model.pb"
-    base_dir = "libs/detectors/x86/data/"
+    base_dir = "/repo/data/x86/"
     model_dir = os.path.join(base_dir, model_name)
     if not os.path.isdir(model_dir):
         os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
diff --git a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
index 30be361e..7eaffcb8 100644
--- a/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
+++ b/libs/detectors/x86/pedestrian_ssdlite_mobilenet_v3.py
@@ -11,7 +11,7 @@
 def load_model(model_name):
     base_url = 'https://raw.githubusercontent.com/neuralet/neuralet-models/master/amd64/'
     model_file = model_name + "/saved_model/saved_model.pb"
-    base_dir = "libs/detectors/x86/data/"
+    base_dir = "/repo/data/x86/"
     model_dir = os.path.join(base_dir, model_name)
     if not os.path.isdir(model_dir):
         os.makedirs(os.path.join(model_dir, "saved_model"), exist_ok=True)
diff --git a/libs/detectors/x86/ssd_mobilenet_v2 b/libs/detectors/x86/ssd_mobilenet_v2.py
similarity index 98%
rename from libs/detectors/x86/ssd_mobilenet_v2
rename to libs/detectors/x86/ssd_mobilenet_v2.py
index 529e9c11..f1fe9e6c 100644
--- a/libs/detectors/x86/ssd_mobilenet_v2
+++ b/libs/detectors/x86/ssd_mobilenet_v2.py
@@ -12,7 +12,7 @@
 def load_model(model_name):
     base_url = 'http://download.tensorflow.org/models/object_detection/'
     model_file = model_name + '.tar.gz'
-    base_dir = "libs/detectors/x86/data/"
+    base_dir = "/repo/data/x86/"
     model_dir = os.path.join(base_dir, model_name)
     if not os.path.isdir(model_dir):
         print('model does not exist under: ', model_dir, 'downloading from ', base_url + model_file)