Added title, minor update

Author: kapil-varshney

guide/14-deep-learning/yolov3_object_detector.ipynb

@@ -4,7 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Introduction"
+    "# YOLOv3 Object Detector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
    ]
   },
   {
@@ -39,7 +46,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "YOLOv3 uses **Darknet-53** as its backbone. This contrasts with the use of popular ResNet family of backbones by other models such as SSD and RetinaNet. Darknet-53 is a deeper version of Darknet-19 which was used in [YOLOv2](https://arxiv.org/pdf/1612.08242.pdf), a prior version. As the name suggests, this backbone architecture has 53 convolutional layers. Adapting the ResNet style residual layers has improved its accuracy but still maintaining the speed advantage. This feature extractor performs better than ResNet101 and similar to ResNet152 while being about 1.5x and 2x faster, respectively [2].\n",
+    "YOLOv3 uses **Darknet-53** as its backbone. This contrasts with the use of popular ResNet family of backbones by other models such as SSD and RetinaNet. Darknet-53 is a deeper version of Darknet-19 which was used in [YOLOv2](https://arxiv.org/pdf/1612.08242.pdf), a prior version. As the name suggests, this backbone architecture has 53 convolutional layers. Adapting the ResNet style residual layers has improved its accuracy while maintaining the speed advantage. This feature extractor performs better than ResNet101 and similar to ResNet152 while being about 1.5x and 2x faster, respectively [2].\n",
     "\n",
     "YOLOv3 has incremental improvements over its prior versions [2]. It uses upsampling and concatenation of feature layers with earlier feature layers which preserve fine-grained features. Another improvement is using three scales for detection. This has made the model good at detecting objects of varying scales in an image. There are other improvements in anchor box selections, loss function, etc. For a detailed analysis of the YOLOv3 architecture, please refer to this [blog](https://towardsdatascience.com/yolo-v3-object-detection-53fb7d3bfe6b)."
    ]