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+# Brevitas QONNX Examples
+
+This repo contains pretrained models in the Quantized ONNX format (QONNX). Originally used in the [BNN-PYNQ](https://github.com/Xilinx/BNN-PYNQ) repo
+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/bnn_pynq) are courtesy of 
+[Alessandro Pappalardo](https://github.com/volcacius) and [Ussama Zahid](https://github.com/ussamazahid96).
+
+## Models
+
+"LFC" models are "large" fully connected networks, similar to "TFC"/"SFC" models, with three hidden layers, each with 64 neurons. 
+More details about these models can be found in the [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://arxiv.org/abs/1612.07119) 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 |
+|----------|------------------------------|---------------------|-------------------------|---------------|---------------|
+| LFC_1W1A | 1 bit                        | 1 bit               | 1 bit                   |  MNIST        |    98.88%     |
+| LFC_1W2A | 2 bit                        | 1 bit               | 2 bit                   |  MNIST        |    98.99%     |
+
+## 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).