diff --git a/2. Training and Detection.ipynb b/2. Training and Detection.ipynb
index 7827d4dd7..f16ced9c0 100644
--- a/2. Training and Detection.ipynb	
+++ b/2. Training and Detection.ipynb	
@@ -3160,13 +3160,103 @@
     "        break"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 11. Real Time Detections from your Webcam"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This part helps you to use your cellphone camera to do the detection\n",
+    "1-Install IP Webcam on your cellphone from app store\n",
+    "2-Run the app and copy the IP that is shown on the screen in the \"URL\" section in the cell below.\n",
+    "3-Run the cell and detect using your cellphone webcam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip uninstall opencv-python-headless -y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import imutils\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"http://192.168.137.34:8080/shot.jpg\"\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
+    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
+    "\n",
+    "while True:\n",
+    "    img_resp = requests.get(url)\n",
+    "    img_arr = np.array(bytearray(img_resp.content), dtype=np.uint8)\n",
+    "    img = cv2.imdecode(img_arr, -1)\n",
+    "    #img = imutils.resize(img, width=800, height=600)\n",
+    "    #cv2.imshow(\"Android_cam\", img)\n",
+    "    image_np = np.array(img)\n",
+    "    \n",
+    "    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
+    "    detections = detect_fn(input_tensor)\n",
+    "    \n",
+    "    num_detections = int(detections.pop('num_detections'))\n",
+    "    detections = {key: value[0, :num_detections].numpy()\n",
+    "                  for key, value in detections.items()}\n",
+    "    detections['num_detections'] = num_detections\n",
+    "\n",
+    "    # detection_classes should be ints.\n",
+    "    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
+    "\n",
+    "    label_id_offset = 1\n",
+    "    image_np_with_detections = image_np.copy()\n",
+    "\n",
+    "    viz_utils.visualize_boxes_and_labels_on_image_array(\n",
+    "                image_np_with_detections,\n",
+    "                detections['detection_boxes'],\n",
+    "                detections['detection_classes']+label_id_offset,\n",
+    "                detections['detection_scores'],\n",
+    "                category_index,\n",
+    "                use_normalized_coordinates=True,\n",
+    "                max_boxes_to_draw=50,\n",
+    "                min_score_thresh=.8,\n",
+    "                agnostic_mode=False)\n",
+    "\n",
+    "    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))\n",
+    "    \n",
+    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
+    "        cap.release()\n",
+    "        cv2.destroyAllWindows()\n",
+    "        break"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "rzlM4jt0pfDJ"
    },
    "source": [
-    "# 10. Freezing the Graph"
+    "# 12. Freezing the Graph"
    ]
   },
   {
@@ -3435,7 +3525,7 @@
     "id": "wTPmdqaXpfDK"
    },
    "source": [
-    "# 11. Conversion to TFJS"
+    "# 13. Conversion to TFJS"
    ]
   },
   {
@@ -3676,7 +3766,7 @@
     "id": "VtUw73FHpfDK"
    },
    "source": [
-    "# 12. Conversion to TFLite"
+    "# 14. Conversion to TFLite"
    ]
   },
   {
@@ -4125,7 +4215,7 @@
     "id": "5NQqZRdA21Uc"
    },
    "source": [
-    "# 13. Zip and Export Models "
+    "# 15. Zip and Export Models "
    ]
   },
   {
@@ -4163,9 +4253,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "tfod",
+   "display_name": "tfodj",
    "language": "python",
-   "name": "tfod"
+   "name": "tfodj"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4177,7 +4267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,