Typos

Author: Gabefire

examples/integrations/yolo/import_yolo_annotations.ipynb

@@ -31,12 +31,12 @@
       "metadata": {},
       "source": [
         "# Import YOLOv8 annotations\n",
-        "This notebook will provides examples of setting up a Project with annotations generated with YOLOv8. We will be using the [Ultralytics](https://docs.ultralytics.com/) library to generate our annotations. In this guide we will be:\n",
-        "* Importing a demo image data rows that will be labeled\n",
-        "* Setting up our ontology that matches our YOLOv8 annotations\n",
-        "* Importing our data rows and attaching our ontology to a project\n",
-        "* Running our images through Ultralytics\n",
-        "* Importing the annotations generated\n"
+        "This notebook will provide examples of setting up a Project with annotations generated with YOLOv8. We will use the [Ultralytics](https://docs.ultralytics.com/) library to generate annotations. In this guide, we will be:\n",
+        "1. Importing a demo image data rows that will be labeled\n",
+        "2. Setting up our ontology that matches our YOLOv8 annotations\n",
+        "3. Importing our data rows and attaching our ontology to a project\n",
+        "4. Running our images through Ultralytics\n",
+        "5. Importing the annotations generated\n"
       ],
       "cell_type": "markdown"
     },
@@ -80,7 +80,7 @@
       "metadata": {},
       "source": [
         "## Set up a YOLOv8 model\n",
-        "Below we will be initializing our model to be used for on our image data rows. We are using `yolov8n-seg.pt` since it supports segmentation masks. "
+        "Below, we will initialize our model for our image data rows. We are using `yolov8n-seg.pt` since it supports segmentation masks. "
       ],
       "cell_type": "markdown"
     },
@@ -96,7 +96,7 @@
       "source": [
         "## Example: Import YOLOv8 Annotations\n",
         "\n",
-        "The first few steps of this guide will demonstrating a basic workflow of creating data rows and setting up a project. For a quick complete overview of this process visit our [Quick start](https://docs.labelbox.com/reference/quick-start) guide."
+        "The first few steps of this guide will demonstrate a basic workflow of creating data rows and setting up a project. For a quick, complete overview of this process, visit our [Quick start](https://docs.labelbox.com/reference/quick-start) guide."
       ],
       "cell_type": "markdown"
     },
@@ -120,7 +120,7 @@
       "metadata": {},
       "source": [
         "### Setting up an ontology and a project\n",
-        "You must create a matching ontology and project with the data rows you are trying to label. The ontology should include the annotations that your are wanting to derive from YOLOv8.. We will be introduce and explain a class mapping later in this guide so feel free to name your ontology features anything you want. In our example, we will be including a combination of bounding boxes, segment mask, and polygon tools to demonstrate converting each of those type of annotations from YOLOv8.\n"
+        "You must create a matching ontology and project with the data rows you are trying to label. The ontology should include the annotations you want to derive from YOLOv8. We will introduce and explain our class mapping later in this guide, so feel free to name your ontology features anything you want. In our example, we will be including a combination of bounding boxes, segment masks, and polygon tools to demonstrate converting each of those types of annotations from YOLOv8.\n"
       ],
       "cell_type": "markdown"
     },
@@ -155,8 +155,8 @@
     {
       "metadata": {},
       "source": [
-        "### Export our data rows and getting our predictions\n",
-        "Now that we have imported our image data row and set up our project and ontology we can now get our predictions. In the step below, we are exporting our data row from our project and then adding the `row_data` and `global_key` to a list to then be used to make our predictions."
+        "### Export our data rows and get our predictions\n",
+        "Now that we have imported our image data row and set up our project and ontology, we can get our predictions. In the step below, we export our data row from our project and then add the `row_data` and `global_key` to a list to be used to make our predictions."
       ],
       "cell_type": "markdown"
     },
@@ -178,7 +178,7 @@
       "metadata": {},
       "source": [
         "#### Make YOLOv8 predictions\n",
-        "Below we are passing our list of image URLs to our YOLOv8 model. If your `row_data` URL are behind a private cloud bucket integration you will need to either download your images locally or give your IDE permission to access your images."
+        "Below, we are passing our list of image URLs to our YOLOv8 model. If your `row_data` URL is behind a private cloud bucket integration, you will need to either download your images locally or give your IDE permission to access them."
       ],
       "cell_type": "markdown"
     },
@@ -193,7 +193,7 @@
       "metadata": {},
       "source": [
         "### Import YOLOv8 annotations to a project\n",
-        "Now that you have finished your initial set up and have gotten your predictions from YOLOv8 we can import our annotations towards are project. We will be doing the following in this step:\n",
+        "Now that you have finished your initial setup and have gotten your predictions from YOLOv8, we can import our annotations towards our project. We will be doing the following in this step:\n",
         "1. Defining our import functions\n",
         "2. Creating our labels\n",
         "3. Importing our labels as either ground truths or MAL labels (pre-labels)"
@@ -204,7 +204,7 @@
       "metadata": {},
       "source": [
         "#### Defining our import functions\n",
-        "YOLOv8 supports a wide range of annotations. This guide shows importing bounding boxes, polygons and segment masks which matches our ontology. Below our the functions used for each type. These functions follow the same similar style, essentially, navigating through our result payload from YOLOv8 and converting it to the Labelbox annotation format. All of our functions support a class mapping which maps YOLOv8 annotation names to Labelbox feature names. The reason we have this mapping is to support having different names for Labelbox features compared to YOLOv8 annotation names. It also allows us to map common YOLOv8 names to the same Labelbox feature attached to our ontology. We will define this mapping first. In our case, we are mapping `bus` and `truck` to our Labelbox feature name `Vehicle` and `person` to our Labelbox feature name `Person`."
+        "YOLOv8 supports a wide range of annotations. This guide shows importing bounding boxes, polygons, and segment masks that match our ontology. Below are the functions used for each type. These functions follow the same style, essentially navigating through our result payload from YOLOv8 and converting it to the Labelbox annotation format. All of our functions support a class mapping which maps YOLOv8 annotation names to Labelbox feature names. The reason we have this mapping is to support having different names for Labelbox features compared to YOLOv8 annotation names. It also allows us to map common YOLOv8 names to the same Labelbox feature attached to our ontology. We will define this mapping first. In our case, we are mapping `bus` and `truck` to our Labelbox feature name `Vehicle` and `person` to our Labelbox feature name `Person`."
       ],
       "cell_type": "markdown"
     },
@@ -261,7 +261,7 @@
       "metadata": {},
       "source": [
         "#### Creating our labels\n",
-        "Now that we have defined our functions to create our Labelbox annotations we can run each result from our YOLOv8 prediction list with our global keys to create our labels. "
+        "Now that we have defined our functions to create our Labelbox annotations, we can run each result from our YOLOv8 prediction list with our global keys to create our labels. "
       ],
       "cell_type": "markdown"
     },
@@ -276,7 +276,7 @@
       "metadata": {},
       "source": [
         "#### Import annotations to Labelbox\n",
-        "We have now created our labels and can import them towards our project. For more information on importing annotations visit our [import image annotations](https://docs.labelbox.com/reference/import-image-annotations) guide."
+        "We have created our labels and can import them to our project. For more information on importing annotations, visit our [import image annotations](https://docs.labelbox.com/reference/import-image-annotations) guide."
       ],
       "cell_type": "markdown"
     },
@@ -324,4 +324,4 @@
       "execution_count": null
     }
   ]
-}
+}