Update QuickNet documentation. (#663)

* Update QuickNet documentation.

* Remove h5 link and update formatting.

* Update RPi4 link and specify clock frequency

* Update [email protected] benchmarks with the latest code (TF2.8) and XNNpack enabled

Co-authored-by: Cedric Nugteren <[email protected]>

Author: AdamHillier

README.md

@@ -45,32 +45,32 @@ advantage of multi-core modern desktop and mobile CPUs.
 
 The table below presents **single-threaded** performance of Larq Compute Engine on
 different versions of a novel BNN model called QuickNet (trained on ImageNet dataset, released on [Larq Zoo](https://docs.larq.dev/zoo/))
-on a [Pixel 1 phone (2016)](https://support.google.com/pixelphone/answer/7158570?hl=en-GB)
-and a Raspberry Pi 4 Model B ([BCM2711](https://www.raspberrypi.org/documentation/hardware/raspberrypi/bcm2711/README.md)) board:
+on a Raspberry Pi 4 Model B at 1.5GHz ([BCM2711](https://www.raspberrypi.com/documentation/computers/processors.html#bcm2711)) board, a [Pixel 1 Android phone (2016)](https://support.google.com/pixelphone/answer/7158570?hl=en-GB), and a [Mac Mini with M1 ARM CPU](https://www.apple.com/uk/mac-mini/):
 
-| Model                                                                                                                                                                      | Top-1 Accuracy | RPi 4 B, ms (1 thread) | Pixel 1, ms (1 thread) |
-| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------: | :--------------------: | :--------------------: |
-| [QuickNet](https://docs.larq.dev/zoo/api/sota/#quicknet) ([.h5](https://github.com/larq/zoo/releases/download/quicknet-v0.2.0/quicknet_weights.h5))                        |     58.6 %     |          31.4          |          16.8          |
-| [QuickNet-Large](https://docs.larq.dev/zoo/api/sota/#quicknetlarge) ([.h5](https://github.com/larq/zoo/releases/download/quicknet_large-v0.2.0/quicknet_large_weights.h5)) |     62.7 %     |          48.7          |          25.5          |
-| [QuickNet-XL](https://docs.larq.dev/zoo/api/sota/#quicknetxl) ([.h5](https://github.com/larq/zoo/releases/download/quicknet_xl-v0.1.0/quicknet_xl_weights.h5))             |     67.0 %     |          82.9          |          44.2          |
+| Model                                                              | Top-1 Accuracy | RPi 4B 1.5GHz, 1 thread (ms) | Pixel 1, 1 thread (ms) | Mac Mini M1, 1 thread (ms) |
+|--------------------------------------------------------------------|----------------|------------------------------|------------------------|----------------------------|
+| [QuickNetSmall](https://docs.larq.dev/zoo/api/sota/#quicknetsmall) | 59.4%          | 27.7                         | 16.8                   | 4.0                        |
+| [QuickNet](https://docs.larq.dev/zoo/api/sota/#quicknet)           | 63.3%          | 45.0                         | 25.5                   | 5.8                        |
+| [QuickNetLarge](https://docs.larq.dev/zoo/api/sota/#quicknetlarge) | 66.9%          | 77.0                         | 44.2                   | 9.9                        |
 
 For reference, [dabnn](https://github.com/JDAI-CV/dabnn) (the other main BNN library) reports an inference time of 61.3 ms for [Bi-RealNet](https://docs.larq.dev/zoo/api/literature/#birealnet) (56.4% accuracy) on the Pixel 1 phone,
 while LCE achieves an inference time of 41.6 ms for Bi-RealNet on the same device.
 They furthermore present a modified version, BiRealNet-Stem, which achieves the same accuracy of 56.4% in 43.2 ms.
 
 The following table presents **multi-threaded** performance of Larq Compute Engine on
-a Pixel 1 phone and a Raspberry Pi 4 Model B ([BCM2711](https://www.raspberrypi.org/documentation/hardware/raspberrypi/bcm2711/README.md))
+a Pixel 1 phone and a Raspberry Pi 4 Model B at 1.5GHz ([BCM2711](https://www.raspberrypi.com/documentation/computers/processors.html#bcm2711))
 board:
 
-| Model                                                                                                                                                                      | Top-1 Accuracy | RPi 4 B, ms (4 threads) | Pixel 1, ms (4 threads) |
-| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------: | :---------------------: | :---------------------: |
-| [QuickNet](https://docs.larq.dev/zoo/api/sota/#quicknet) ([.h5](https://github.com/larq/zoo/releases/download/quicknet-v0.2.0/quicknet_weights.h5))                        |     58.6 %     |          16.1           |           8.9           |
-| [QuickNet-Large](https://docs.larq.dev/zoo/api/sota/#quicknetlarge) ([.h5](https://github.com/larq/zoo/releases/download/quicknet_large-v0.2.0/quicknet_large_weights.h5)) |     62.7 %     |          24.7           |          12.6           |
-| [QuickNet-XL](https://docs.larq.dev/zoo/api/sota/#quicknetxl) ([.h5](https://github.com/larq/zoo/releases/download/quicknet_xl-v0.1.0/quicknet_xl_weights.h5))             |     67.0 %     |          37.9           |          22.8           |
+| Model                                                              | Top-1 Accuracy | RPi 4B 1.5GHz, 4 threads (ms) | Pixel 1, 4 threads (ms) | Mac Mini M1, 4 threads (ms) |
+|--------------------------------------------------------------------|----------------|-------------------------------|-------------------------|-----------------------------|
+| [QuickNetSmall](https://docs.larq.dev/zoo/api/sota/#quicknetsmall) | 59.4%          | 12.1                          | 8.9                     | 1.8                         |
+| [QuickNet](https://docs.larq.dev/zoo/api/sota/#quicknet)           | 63.3%          | 20.8                          | 12.6                    | 2.5                         |
+| [QuickNetLarge](https://docs.larq.dev/zoo/api/sota/#quicknetlarge) | 66.9%          | 31.7                          | 22.8                    | 3.9                         |
 
-Benchmarked on August 21st, 2020 with LCE custom
+Benchmarked on 2021-06-11 (Pixel 1), 2021-06-13 (Mac Mini M1), and 2022-04-20 (RPi 4B) with LCE custom
 [TFLite Model Benchmark Tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark)
 (see [here](https://github.com/larq/compute-engine/tree/main/larq_compute_engine/tflite/benchmark))
+with XNNPack enabled
 and BNN models with randomized inputs.
 
 ## Getting started