Merge pull request #2 from fastmachinelearning/feature/add_mobilnet_and_bipolar_nets

Updated TFC and CNV models, added mobilenet. Closes #1

Author: ben-hawks

models/CIFAR10/Brevitas_FINN_CNV/CNV_1W1A.onnx

@@ -21,8 +21,8 @@ NOTE: Accuracy numbers listed here are taken from the Brevitas repo, and are not
 
 ## Train
 
-The training scripts for these models are located in the [Brevitas Repo](https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/bnn_pynq)
+The training scripts for these models are located in the [Brevitas Repo](https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/bnn_pynq).
 
 ## License
 
-Models are licensed under LICENSE.md within this directory
+Models are licensed under LICENSE.txt within this directory.

models/CIFAR10/Brevitas_FINN_CNV/CNV_1W2A.onnx

@@ -0,0 +1,39 @@
+Copyright (c) 2018-     Xilinx, Inc              (Alessandro Pappalardo)
+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+3. Neither the names of Xilinx, Facebook, Deepmind Technologies, NYU, 
+   NEC Laboratories America and IDIAP Research Institute nor the names 
+   of its contributors may be used to endorse or promote products derived 
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.

models/CIFAR10/Brevitas_FINN_CNV/CNV_2W2A.onnx

@@ -0,0 +1,24 @@
+# Brevitas QONNX Examples
+
+This repo contains pretrained models in the Quantized ONNX format (QONNX), which were trained and exported using [Brevitas](https://github.com/Xilinx/brevitas).
+
+These pretrained models and training scripts (located in the [Brevitas Repo](https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/imagenet_classification) are courtesy of 
+[Alessandro Pappalardo](https://github.com/volcacius).
+
+## Models
+
+The mobilenet model is based on the MobilnNets V1 architecture, published in the [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) paper.
+
+NOTE: Accuracy numbers listed here are taken from the Brevitas repo, and are not verified running through QONNX at this time. 
+
+| Name           | Input quantization | Weight quantization | Activation quantization | Dataset  | Top1 accuracy | Top5 accuracy |
+|----------------|--------------------|---------------------|-------------------------|----------|---------------|---------------|
+| mobilenet_4W4A | 8 bit              | 4 bit               | 4 bit                   | ImageNet | 84.22%        | 90.10%        |
+
+## Train
+
+The training scripts for these models are located in the [Brevitas Repo](https://github.com/Xilinx/brevitas).
+
+## License
+
+Models are licensed under LICENSE.txt within this directory.

models/CIFAR10/Brevitas_FINN_CNV/LICENSE.md renamed to models/CIFAR10/Brevitas_FINN_CNV/LICENSE.txt

@@ -21,8 +21,8 @@ NOTE: Accuracy numbers listed here are taken from the Brevitas repo, and are not
 
 ## Train
 
-The training scripts for these models are located in the [Brevitas Repo](https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/bnn_pynq)
+The training scripts for these models are located in the [Brevitas Repo](https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/bnn_pynq).
 
 ## License
 
-Models are licensed under LICENSE.md within this directory
+Models are licensed under LICENSE.txt within this directory.