message: Added overview section for 16x8 quantization.
diff: @@ -125,6 +125,37 @@ the numbers here:
  </figcaption>
  </figure>
  +### Full integer quantization with int16 activations and int8 weights
  +
  +[Quantization with int16 activations](https://www.tensorflow.org/model_optimization/guide/quantization/post_training) is a full integer quantization scheme with activations in int16 and weights in int8. This mode can improve accuracy of the quantized model in comparison to the full integer quantization scheme with both activations and weights in int8 keeping a similar model size. It is recommended when activations are sensitive to the quantization.
  +
  +Below are the accuracy results for some models that benefit from this mode.
  +<figure>
  + <table>
  + <tr>
  + <th>Model</th>
  + <th>Accuracy metric type </th>
  + <th>Accuracy (float32 activations) </th>
  + <th>Accuracy (int8 activations) </th>
  + <th>Accuracy (int16 activations) </th>
  + </tr> <tr><td>Wav2letter</td><td>WER</td><td>6.7%</td><td>7.7%</td>
  + <td>7.2%</td></tr>
  + <tr><td>DeepSpeech 0.51 (unrolled)</td><td>CER</td><td>6.13%</td><td>43.67%</td>
  + <td>6.52%</td></tr>
  + <tr><td>YoloV3</td><td>mAP(IOU=0.5)</td><td>0.577</td><td>0.563</td>
  + <td>0.574</td></tr>
  + <tr><td>MobileNetV1</td><td>Top-1 Accuracy</td><td>0.7062</td><td>0.694</td>
  + <td>0.6936</td></tr>
  + <tr><td>MobileNetV2</td><td>Top-1 Accuracy</td><td>0.718</td><td>0.7126</td>
  + <td>0.7137</td></tr>
  + <tr><td>MobileBert</td><td>F1(Exact match)</td><td>88.81(81.23)</td><td>2.08(0)</td>
  + <td>88.73(81.15)</td></tr>
  + </table>
  + <figcaption>
  + <b>Table 2</b> Benefits of model quantization with int16 activations
  + </figcaption>
  +</figure>
  +
  ### Pruning
  [Pruning](https://www.tensorflow.org/model_optimization/guide/pruning) works by
  