add description

Author: dnth

examples/analyzing-object-detection-dataset.ipynb

@@ -29,8 +29,7 @@
     "id": "3c6dffd7"
    },
    "source": [
-    "## Installation & Setting Up\n",
-    "This notebook is written to be run on Google Colab. If you're running fastdup locally, view the installation instructions for your operating system here."
+    "## Installation"
    ]
   },
   {
@@ -49,6 +48,14 @@
     "!pip install -Uq fastdup plotly gdown"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7c841ee4-d27b-4195-939b-8b44be924518",
+   "metadata": {},
+   "source": [
+    "Now, test the installation by printing out the version. If there's no error message, we are ready to go!"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -93,7 +100,9 @@
    },
    "source": [
     "## Download Dataset\n",
-    "We will be using the [COCO minitrain](https://github.com/giddyyupp/coco-minitrain) dataset for this tutorial. Let's download the dataset into our local drive."
+    "We will be using the [COCO minitrain](https://github.com/giddyyupp/coco-minitrain) dataset for this tutorial. COCO minitrain is a curated mini training set with about 25,000 images or 20% of the original [COCO dataset](https://cocodataset.org/#home).\n",
+    "\n",
+    "Let's download the dataset into our local drive."
    ]
   },
   {
@@ -125,6 +134,8 @@
    },
    "source": [
     "## 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."
    ]
   },