Minor updates + updated image

Author: kapil-varshney

guide/14-deep-learning/yolov3_object_detector.ipynb

@@ -13,9 +13,9 @@
    "source": [
     "**YOLO (You Only Look Once)** is one of the most popular series of object detection models. Its advantage has been in providing real-time detections while approaching the accuracy of state-of-the-art object detection models.\n",
     "\n",
-    "In the earlier works for object detection, models used to either use a sliding window technique or region proposal network. Sliding window, as the name suggests choses a Region of Interest (RoI) by sliding a window across the image and then performs classification in the chosen RoI to detect an object. Region proposal networks work in two steps - first, they extract region proposals and then using CNN features, classify the proposed regions. Sliding window method is not very precise and accurate and though some of the region-based networks can be highly accurate they tend to be slower.\n",
+    "In the earlier works for object detection, models used to either use a sliding window technique or region proposal network. Sliding window, as the name suggests choses a Region of Interest (RoI) by sliding a window across the image and then performs classification in the chosen RoI to detect an object. Region proposal networks work in two steps - first, they extract region proposals and then using CNN features, classify the proposed regions. Sliding window method is not very precise and accurate, and though some of the region-based networks can be highly accurate they tend to be slower.\n",
     "\n",
-    "Then came along the one-shot object detectors such as [SSD](https://arxiv.org/abs/1512.02325), [YOLO](https://arxiv.org/pdf/1506.02640.pdf) and [RetinaNet](https://arxiv.org/abs/1708.02002). These models detect objects in a single pass of the image and, thus, are considerably faster, and can match up the accuracy of region-based detectors. The [SSD guide](https://developers.arcgis.com/python/guide/how-ssd-works/) explains the essentials components of a one-shot object detection model. You can also read up the RetinaNet guide [here](https://developers.arcgis.com/python/guide/how-retinanet-works/). These models are already a part of ArcGIS API for Python and the addition of [**YOLOv3**](https://arxiv.org/abs/1804.02767) provides another tool in our deep learning toolbox.\n",
+    "Then came along the one-shot object detectors such as [SSD](https://arxiv.org/abs/1512.02325), [YOLO](https://arxiv.org/pdf/1506.02640.pdf) and [RetinaNet](https://arxiv.org/abs/1708.02002). These models detect objects in a single pass of the image and, thus, are considerably faster, and can match up the accuracy of region-based detectors. The [SSD guide](https://developers.arcgis.com/python/guide/how-ssd-works/) explains the essential components of a one-shot object detection model. You can also read up the RetinaNet guide [here](https://developers.arcgis.com/python/guide/how-retinanet-works/). These models are already a part of ArcGIS API for Python and the addition of [**YOLOv3**](https://arxiv.org/abs/1804.02767) provides another tool in our deep learning toolbox.\n",
     "\n",
     "The biggest advantage of YOLOv3 in `arcgis.learn` is that it comes preloaded with weights pretrained on the [COCO dataset](https://cocodataset.org/). This makes it ready-to-use for the 80 common objects (car, truck, person, etc.) that are part of the COCO dataset."
    ]