Address Francois' comments

scripts/cv_api_master.py

@@ -107,14 +107,14 @@
 }
 
 BOUNDING_BOX_FORMATS = {
-        "path": "formats",
-        "title": "Bounding box formats",
-        "generate": [
-            "keras_cv.bounding_box.CENTER_XYWH",
-            "keras_cv.bounding_box.XYWH",
-            "keras_cv.bounding_box.XYXY",
-            "keras_cv.bounding_box.REL_XYXY",
-        ],
+    "path": "formats",
+    "title": "Bounding box formats",
+    "generate": [
+        "keras_cv.bounding_box.CENTER_XYWH",
+        "keras_cv.bounding_box.XYWH",
+        "keras_cv.bounding_box.XYXY",
+        "keras_cv.bounding_box.REL_XYXY",
+    ],
 }
 
 BOUNDING_BOX_UTILS = {
@@ -134,10 +134,7 @@
     "path": "bounding_box/",
     "title": "Bounding box formats and utilities",
     "toc": True,
-    "children": [
-        BOUNDING_BOX_FORMATS,
-        BOUNDING_BOX_UTILS
-    ]
+    "children": [BOUNDING_BOX_FORMATS, BOUNDING_BOX_UTILS],
 }
 
 REGULARIZATION_MASTER = {

templates/keras_cv/bounding_box/index.md

@@ -1,8 +1,10 @@
 # KerasCV Bounding Boxes
+
 All KerasCV components that process bounding boxes require a `bounding_box_format`
 argument.  This argument allows you to seamlessly integrate KerasCV components into
 your own workflows while preserving proper behavior of the components themselves.
 
-[The list of bounding box formats supported in KerasCV are listed in the API docs](/api/keras_cv/bounding_box/formats).
+The bounding box formats supported in KerasCV 
+[are listed in the API docs](/api/keras_cv/bounding_box/formats)
 If a format you would like to use is missing,
 [feel free to open a GitHub issue on KerasCV](https://github.com/keras-team/keras-cv/issues)!

templates/keras_cv/index.md

@@ -26,19 +26,21 @@ You can also check out other versions in our
 [GitHub repository](https://github.com/keras-team/keras-cv/releases).
 
 ## Quick Introduction
+
 Create a preprocessing pipeline:
+
 ```python
 import keras_cv
-import tensorflow as tf
+from tensorflow import keras
 
-preprocessing_model = tf.keras.Sequential([
+preprocessing_model = keras.Sequential([
     keras_cv.layers.RandAugment(value_range=(0, 255))
     keras_cv.layers.CutMix(),
     keras_cv.layers.MixUp()
 ], name="preprocessing_model")
 ```
 
-Augment a TensorFlow dataset:
+Augment a `tf.data.Dataset`:
 
 ```python
 dataset = dataset.map(lambda images, labels: {"images": images, "labels": labels})
@@ -47,6 +49,7 @@ dataset = dataset.map(lambda inputs: (inputs["images"], inputs["labels"]))
 ```
 
 Create a model:
+
 ```python
 densenet = keras_cv.models.DenseNet(
   include_rescaling=True,
@@ -56,7 +59,8 @@ densenet = keras_cv.models.DenseNet(
 densenet.compile(optimizer='adam', metrics=['accuracy'])
 ```
 
-Fit your model:
+Train your model:
+
 ```python
 densenet.fit(dataset)
 ```