3rd place solution for NeurIPS 2019 MicroNet challenge

Submission name: QualcommAI-EfficientNet. MicroNet Challenge (NeurIPS 2019) submission - Qualcomm AI Research

Experimental Adversarial Attack notebooks on CV models

Image classification done with Mindspore technology

FrostNet: Towards Quantization-Aware Network Architecture Search

Our work implements novel L2-Norm gradient (L2Grad) and variance of the weight distrbution (VarianceNorm) regularizers for quantization-aware training such that the distribution of weights are more compatible with post-training quantization especially for low bit-widths. We provide a theoretical basis that directly relates L2-Grad with post quan…

A simple formula supports eight types of quantization

A tutorial of model quantization using TensorFlow

8 bit quantizated Transformer for neural machine translation.

all methods of pytorch quantization based on resnet50

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、reg…

Visualizing DNN Quantization effect on Network.

FakeQuantize with Learned Step Size(LSQ+) as Observer in PyTorch

Quantization-aware training with spiking neural networks

Code for paper 'Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware'

Transformer quantization and binarization exploration

One Bit at a Time: Impact of Quantisation on Neural Machine Translation

Quantization for Object Detection in Tensorflow 2.x

CNN quantization

Low-Precision Neural Networks for Classification on PYNQ with FINN