diff --git a/docs/solutions/object_detection.md b/docs/solutions/object_detection.md
index f08d0a928..eea17d3cb 100644
--- a/docs/solutions/object_detection.md
+++ b/docs/solutions/object_detection.md
@@ -118,9 +118,9 @@ on how to build MediaPipe examples.
 *   With a TensorFlow Model
 
     This uses the
-    [TensorFlow model](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model)
+    [TensorFlow model](https://github.com/google/mediapipe/tree/v0.8.10/mediapipe/models/object_detection_saved_model)
     ( see also
-    [model info](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model/README.md)),
+    [model info](https://github.com/google/mediapipe/tree/master/mediapipe/g3doc/solutions/object_detection_saved_model.md)),
     and the pipeline is implemented in this
     [graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt).
 
diff --git a/docs/solutions/object_detection_saved_model.md b/docs/solutions/object_detection_saved_model.md
new file mode 100644
index 000000000..6acac0a1b
--- /dev/null
+++ b/docs/solutions/object_detection_saved_model.md
@@ -0,0 +1,62 @@
+## TensorFlow/TFLite Object Detection Model
+
+### TensorFlow model
+
+The model is trained on [MSCOCO 2014](http://cocodataset.org) dataset using [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection). It is a MobileNetV2-based SSD model with 0.5 depth multiplier. Detailed training configuration is in the provided `pipeline.config`. The model is a relatively compact model which has `0.171 mAP` to achieve real-time performance on mobile devices. You can compare it with other models from the [TensorFlow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md).
+
+
+### TFLite model
+
+The TFLite model is converted from the TensorFlow above. The steps needed to convert the model are similar to [this tutorial](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193) with minor modifications. Assuming now we have a trained TensorFlow model which includes the checkpoint files and the training configuration file, for example the files provided in this repo:
+
+   * `model.ckpt.index`
+   * `model.ckpt.meta`
+   * `model.ckpt.data-00000-of-00001`
+   * `pipeline.config`
+
+Make sure you have installed these [python libraries](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1.md). Then to get the frozen graph, run the `export_tflite_ssd_graph.py` script from the `models/research` directory with this command:
+
+```bash
+$ PATH_TO_MODEL=path/to/the/model
+$ bazel run object_detection:export_tflite_ssd_graph -- \
+    --pipeline_config_path ${PATH_TO_MODEL}/pipeline.config \
+    --trained_checkpoint_prefix ${PATH_TO_MODEL}/model.ckpt \
+    --output_directory ${PATH_TO_MODEL} \
+    --add_postprocessing_op=False
+```
+
+The exported model contains two files:
+
+   * `tflite_graph.pb`
+   * `tflite_graph.pbtxt`
+
+The difference between this step and the one in [the tutorial](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193) is that we set `add_postprocessing_op` to False. In MediaPipe, we have provided all the calculators needed for post-processing such that we can exclude the custom TFLite ops for post-processing in the original graph, e.g., non-maximum suppression. This enables the flexibility to integrate with different post-processing algorithms and implementations.
+
+Optional: You can install and use the [graph tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms) to inspect the input/output of the exported model:
+
+```bash
+$ bazel run graph_transforms:summarize_graph -- \
+    --in_graph=${PATH_TO_MODEL}/tflite_graph.pb
+```
+
+You should be able to see the input image size of the model is 320x320 and the outputs of the model are:
+
+   * `raw_outputs/box_encodings`
+   * `raw_outputs/class_predictions`
+
+The last step is to convert the model to TFLite. You can look at [this guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/r1/convert/cmdline_examples.md) for more detail. For this example, you just need to run:
+
+```bash
+$ tflite_convert --  \
+  --graph_def_file=${PATH_TO_MODEL}/tflite_graph.pb \
+  --output_file=${PATH_TO_MODEL}/model.tflite \
+  --input_format=TENSORFLOW_GRAPHDEF \
+  --output_format=TFLITE \
+  --inference_type=FLOAT \
+  --input_shapes=1,320,320,3 \
+  --input_arrays=normalized_input_image_tensor \
+  --output_arrays=raw_outputs/box_encodings,raw_outputs/class_predictions
+
+```
+
+Now you have the TFLite model `model.tflite` ready to use with MediaPipe Object Detection graphs. Please see the examples for more detail.
diff --git a/docs/solutions/pose.md b/docs/solutions/pose.md
index 3226ddc2f..ce0197ebd 100644
--- a/docs/solutions/pose.md
+++ b/docs/solutions/pose.md
@@ -269,6 +269,7 @@ Supported configuration options:
 ```python
 import cv2
 import mediapipe as mp
+import numpy as np
 mp_drawing = mp.solutions.drawing_utils
 mp_drawing_styles = mp.solutions.drawing_styles
 mp_pose = mp.solutions.pose