message: Introduce Task library in the modeling page of object detection
diff: @@ -14,18 +14,13 @@ annotated:
  ## Get started
  -If you are new to TensorFlow Lite and are working with Android or iOS, download
  -the following example applications to get started.
  -
  -<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android">Android
  -example</a>
  -<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/ios">iOS
  -example</a>
  +To learn how to use object detection in a mobile app, explore the
  +<a href="#example_applications_and_guides">Example applications and guides</a>.
  If you are using a platform other than Android or iOS, or if you are already
  familiar with the
  <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite
  -APIs</a>, you can download the starter object detection model and the
  +APIs</a>, you can download our starter object detection model and the
  accompanying labels.
  <a class="button button-primary" href="https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/1?lite-format=tflite">Download
  @@ -45,6 +40,38 @@ For the following use cases, you should use a different type of model:
  <li>Predicting the composition of an image, for example subject versus background (see <a href="../segmentation/overview.md">segmentation</a>)</li>
  </ul>
  +### Example applications and guides
  +
  +If you are new to TensorFlow Lite and are working with Android or iOS, we
  +recommend exploring the following example applications that can help you get
  +started.
  +
  +#### Android
  +
  +You can leverage the out-of-box API from TensorFlow Lite Task Library to
  +[integrate object detection models](../../inference_with_metadata/task_library/object_detector)
  +in just a few lines of code. You can also
  +[build your own custom inference pipeline](../../guide/inference#load_and_run_a_model_in_java)
  +using the TensorFlow Lite Interpreter Java API.
  +
  +The Android example below demonstrates the implementation for both methods as
  +[lib_task_api](https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android/lib_task_api)
  +and
  +[lib_interpreter](https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android/lib_interpreter),
  +respectively.
  +
  +<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android">View
  +Android example</a>
  +
  +#### iOS
  +
  +You can integrate the model using the
  +[TensorFlow Lite Interpreter Swift API](../../guide/inference#load_and_run_a_model_in_swift).
  +See the iOS example below.
  +
  +<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/ios">View
  +iOS example</a>
  +
  ## Model description
  This section describes the signature for
  