typo

Author: dnth

examples/analyzing-object-detection-dataset.ipynb

@@ -19,7 +19,7 @@
     "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb)\n",
     "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb)\n",
     "\n",
-    "In this tutorial, we will analyze an object detection dataset with bounding boxes and identify potential issues."
+    "In this tutorial, we will analyze an object detection dataset with bounding boxes and identify potential issues. By the end of the notebook, you'll discover how to load a COCO format bounding box annotations into fastdup and inspect the dataset for issues at the bounding box level."
    ]
   },
   {
@@ -133,7 +133,7 @@
     "id": "8391ef5e"
    },
    "source": [
-    "## Load annotations\n",
+    "## Load Annotations\n",
     "fastdup expects the annotations to be in a specific format.\n",
     "\n",
     "We will use a simple converter to convert the COCO format JSON annotation file into the fastdup annotation dataframe. This converter is applicable to any dataset which uses COCO format."
@@ -166,7 +166,7 @@
     "\n",
     "coco_annotations['split'] = 'train'  # Only train files were loaded\n",
     "coco_annotations['filename'] = coco_annotations['filename'].apply(lambda x: 'coco_minitrain_25k/images/train2017/'+x)\n",
-    "coco_annotations = coco_annotations.drop_duplicates()\n"
+    "coco_annotations = coco_annotations.drop_duplicates()"
    ]
   },
   {
@@ -184900,7 +184900,7 @@
     "id": "953af7a0"
    },
    "source": [
-    "## Component gallery\n",
+    "## Components Gallery\n",
     "View a components gallery and sort by largest objects."
    ]
   },