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+# Weight clustering
+
+This document provides an overview on weight clustering to help you determine how it fits with your use case.
+
+- To dive right into an end-to-end example, see the [weight clustering example](clustering_example.ipynb).
+- To quickly find the APIs you need for your use case, see the [weight clustering comprehensive guide](clustering_comprehensive_guide.ipynb).
+
+## Overview
+
+Clustering, or weight sharing, reduces the number of unique weight values in a model, leading to benefits for deployment. It first groups the weights of each layer into *N* clusters, then shares the cluster's centroid value for all the weights belonging to the cluster.
+
+This technique brings improvements via model compression. Future framework support can unlock memory footprint improvements that can make a crucial difference for deploying deep learning models on embedded systems with limited resources.
+
+We have experimented with clustering across vision and speech tasks. We've seen up to 5x improvements in model compression with minimal loss of accuracy, as demonstrated by the [results](#results) presented below.
+
+Please note that clustering will provide reduced benefits for convolution and dense layers that precede a batch normalization layer, as well as in combination with per-axis post-training quantization.
+
+### API compatibility matrix
+
+Users can apply clustering with the following APIs:
+
+*   Model building: `tf.keras` with only Sequential and Functional models
+*   TensorFlow versions: TF 1.x for versions 1.14+ and 2.x.
+    *   `tf.compat.v1` with a TF 2.X package and `tf.compat.v2` with a TF 1.X
+        package are not supported.
+*   TensorFlow execution mode: both graph and eager
+
+## Results
+
+### Image classification
+
+<table>
+  <tr>
+    <th rowspan="2">Model</th>
+    <th colspan="2">Original</th>
+    <th colspan="4">Clustered</th>
+  </tr>
+  <tr>
+  <th>Top-1 accuracy (%)</th>
+    <th>Size of compressed .tflite (MB)</th>
+    <th>Configuration</th>
+    <th># of clusters</th>
+    <th>Top-1 accuracy (%)</th>
+    <th>Size of compressed .tflite (MB)</th>
+  </tr>
+  <tr>
+    <td rowspan="3">MobileNetV1</td>
+    <td rowspan="3">71.02</td>
+    <td rowspan="3">14.96</td>
+  </tr>
+  <tr>
+    <td>Selective (last 3 Conv2D layers)</td>
+    <td>256, 256, 32</td>
+    <td>70.62</td>
+    <td>8.42</td>
+  </tr>
+  <tr>
+    <td>Full (all Conv2D layers)</td>
+    <td>64</td>
+    <td>66.07</td>
+    <td>2.98</td>
+  </tr>
+  <tr>
+    <td rowspan="3">MobileNetV2</td>
+    <td rowspan="3">72.29</td>
+    <td rowspan="3">12.90</td>
+  </tr>
+  <tr>
+    <td>Selective (last 3 Conv2D layers)</td>
+    <td>256, 256, 32</td>
+    <td>72.31</td>
+    <td>7.00</td>
+ </tr>
+ <tr>
+   <td>Full (all Conv2D layers)</td>
+   <td>32</td>
+   <td>69.33</td>
+   <td>2.60</td>
+  </tr>
+</table>
+
+The models were trained and tested on ImageNet.
+
+### Keyword spotting
+
+<table>
+  <tr>
+    <th rowspan=2>Model</th>
+    <th colspan=2>Original</th>
+    <th colspan=4>Clustered</th>
+  </tr>
+  <tr>
+    <th>Top-1 accuracy (%)</th>
+    <th>Size of compressed .tflite (MB)</th>
+    <th>Configuration</th>
+    <th># of clusters</th>
+    <th>Top-1 accuracy (%)</th>
+    <th>Size of compressed .tflite (MB)</th>
+  </tr>
+  <tr>
+    <td>DS-CNN-L</td>
+    <td>95.03</td>
+    <td>1.5</td>
+    <td>Full</td>
+    <td>32</td>
+    <td>94.71</td>
+    <td>0.3</td>
+  </tr>
+</table>
+
+The models were trained and tested on SpeechCommands v0.02.
+
+NOTE: *Size of compressed .tflite* refers to the size of the zipped .tflite file obtained from the model from the following process:
+1. Serialize the Keras model into .h5 file
+2. Convert the .h5 file into .tflite using `TFLiteConverter.from_keras_model_file()`
+3. Compress the .tflite file into a zip
+
+## Examples
+
+In addition to the [Weight clustering in Keras example](clustering_example.ipynb.ipynb), see the following examples:
+
+* Cluster the weights of a CNN model trained on the MNIST handwritten digit classification dataset:
+[code](https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/python/examples/clustering/keras/mnist/mnist_cnn.py)
+
+The weight clustering implementation is based on the *Deep Compression: Compressing Deep Neural Networks With Pruning, Trained Quantization and Huffman Coding* [paper](https://arxiv.org/abs/1510.00149). See chapter 3, titled *Trained Quantization and Weight Sharing*.
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