fix typos in object detection notebook (cleanlab#983)

huiwengoh · Feb 8, 2024 · 6c1882b · 6c1882b
1 parent 5540959
commit 6c1882b
Showing 1 changed file with 3 additions and 3 deletions.
diff --git a/docs/source/tutorials/object_detection.ipynb b/docs/source/tutorials/object_detection.ipynb
@@ -507,9 +507,9 @@
    "id": "46d6282a-4601-4cc3-b8a8-187ea6d5f8bc",
    "metadata": {},
    "source": [
-    "## Exploratory data analysys\n",
+    "## Exploratory data analysis\n",
     "\n",
-    "This section focuses on techniques to uncover annotation irregularities through exploratory data analysis. Specifically, it aims to highlight anomalies in object sizes, detect images with unusual object counts, and examine the distribution of class labels.\n",
+    "This bonus section considers techniques to uncover annotation irregularities through exploratory data analysis. Specifically, we consider anomalies in object sizes, detect images with unusual object counts, and examine the distribution of class labels.\n",
     "\n",
     "Let's first consider the number of objects per image, and inspect the images with the largest values (which might reveal something off in our dataset):"
    ]
@@ -571,7 +571,7 @@
    "id": "200cdebf-b24c-4c2b-8914-6a2fce218daf",
    "metadata": {},
    "source": [
-    "Finally, let's consider the distribution of bounding box sizes (aka object sizes) in the given annotations for each class label.  The idea is to review any anomalies in bounding box areas for a given class (which might reveal problematic annotations or abnormal instances of this object class).  The following code determines such anomalies by comparing where each bounding box's area falls in comparison to the  on mean and standard deviation of areas for bounding boxes with the same class label."
+    "Finally, let's consider the distribution of bounding box sizes (aka object sizes) in the given annotations for each class label.  The idea is to review any anomalies in bounding box areas for a given class (which might reveal problematic annotations or abnormal instances of this object class).  The following code determines such anomalies by assessing each bounding box's area vs. the mean and standard deviation of areas for bounding boxes with the same class label."
    ]
   },
   {