message: Release benchmarks of OVIC baseline models.
diff: @@ -206,6 +206,45 @@ operation, and running the op under the test harness. For more details see the
  NetAdapt paper<sup>1</sup>. We plan to expand table regularly as we test with
  newer OS releases and updates to Tensorflow Lite.
  +
  +### Sample benchmarks
  +
  +Below are the baseline models (8-bit quantized MobilenetV2 and floating point
  +MnasNet) used to compute the reference accuracy for ImageNet classification. The
  +naming convention of the models are `[model class]_[resolution]_[multiplier]`.
  +Latency (ms) is measured on a single Pixel 2 big core using the competition
  +server on April 30th 2019.
  +
  +Model | Latency | Top-1 Accuracy
  +:-----------------------: | :-----: | :------------:
  +quant_mobilenetv2_96_35 | 4 | 0.420
  +quant_mobilenetv2_96_50 | 6 | 0.478
  +quant_mobilenetv2_128_35 | 7 | 0.474
  +quant_mobilenetv2_128_50 | 10 | 0.546
  +quant_mobilenetv2_96_75 | 10 | 0.560
  +quant_mobilenetv2_160_35 | 11 | 0.534
  +quant_mobilenetv2_96_100 | 13 | 0.579
  +quant_mobilenetv2_160_50 | 15 | 0.583
  +quant_mobilenetv2_192_35 | 16 | 0.557
  +quant_mobilenetv2_128_75 | 17 | 0.611
  +float_mnasnet_96_100 | 19 | 0.625
  +quant_mobilenetv2_128_100 | 20 | 0.629
  +quant_mobilenetv2_192_50 | 20 | 0.616
  +quant_mobilenetv2_224_35 | 21 | 0.581
  +quant_mobilenetv2_160_75 | 26 | 0.646
  +quant_mobilenetv2_224_50 | 28 | 0.637
  +quant_mobilenetv2_160_100 | 31 | 0.674
  +float_mnasnet_224_50 | 36 | 0.679
  +quant_mobilenetv2_192_75 | 36 | 0.674
  +quant_mobilenetv2_192_100 | 44 | 0.695
  +float_mnasnet_160_100 | 46 | 0.706
  +quant_mobilenetv2_224_75 | 50 | 0.684
  +float_mnasnet_224_75 | 59 | 0.718
  +quant_mobilenetv2_224_100 | 60 | 0.704
  +float_mnasnet_192_100 | 66 | 0.724
  +float_mnasnet_224_100 | 91 | 0.742
  +float_mnasnet_224_130 | 135 | 0.758
  +
  ### References
  1. **NetAdapt: Platform-Aware Neural Network Adaptation for Mobile
  