Topic: quantization-aware-training Goto Github
Some thing interesting about quantization-aware-training
Some thing interesting about quantization-aware-training
quantization-aware-training,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、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
User: 666dzy666
quantization-aware-training,TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
Organization: alibaba
quantization-aware-training,0️⃣1️⃣🤗 BitNet-Transformers: Huggingface Transformers Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch with Llama(2) Architecture
User: beomi
quantization-aware-training,Code repository for the paper Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
Organization: bgucompsci
Home Page: https://bgucompsci.github.io/CNNQuantizationThroughPDEs/
quantization-aware-training,Code for paper 'Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware'
User: bharathsudharsan
quantization-aware-training,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 quantization test accuracy through a first order Taylor Series expansion followed by the reduction to an adversary with an L2 budget, in which we apply the Cauchy-Schwarz inequality to provide the desired bounds. We empirically show that L2Grad and VarianceNorm can both match the performance of L1Grad and outperform it on certain bit-widths. We also show that a regularization scheme that combines L2Grad and VarianceNorm in a novel "regularization scheduling" methodology can give even better results in terms of post-quantization accuracy, tested on uniform and piecewise linear quantization.
User: brandontang892
quantization-aware-training,FrostNet: Towards Quantization-Aware Network Architecture Search
Organization: clovaai
Home Page: https://arxiv.org/abs/2006.09679
quantization-aware-training,ECQx: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
User: d-becking
Home Page: https://link.springer.com/chapter/10.1007/978-3-031-04083-2_14
quantization-aware-training,FakeQuantize with Learned Step Size(LSQ+) as Observer in PyTorch
User: deadat0m
quantization-aware-training, Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
User: etetteh
quantization-aware-training,Tutorial notebooks for hls4ml
Organization: fastmachinelearning
Home Page: http://fastmachinelearning.org/hls4ml-tutorial/
quantization-aware-training,
User: gulabpatel
quantization-aware-training,BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural Networks, ECCV 2022
User: hanbyulkim
quantization-aware-training,Visualizing DNN Quantization effect on Network.
User: hanbyulkim
quantization-aware-training,A tutorial of model quantization using TensorFlow
User: haoranren
quantization-aware-training,Notes on quantization in neural networks
User: hkproj
Home Page: https://www.youtube.com/watch?v=0VdNflU08yA
quantization-aware-training,8 bit quantizated Transformer for neural machine translation.
User: iabd
quantization-aware-training,SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
Organization: intel
Home Page: https://intel.github.io/neural-compressor/
quantization-aware-training,Code for the ISCAS23 paper "The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks"
Organization: intelligent-microsystems-lab
quantization-aware-training,Quantization Aware Training
User: jahongir7174
quantization-aware-training,Quantization Aware Training
User: jahongir7174
quantization-aware-training,Quantization-aware training with spiking neural networks
User: jeshraghian
quantization-aware-training,QAT tensorrt量化
User: lix19937
quantization-aware-training,Autoencoder model for FPGA implementation using hls4ml. Repository for Applied Electronics Project.
User: lorenzovalente3
quantization-aware-training,Disentangle joint continous and discrete representations for Anomaly Detection in High Energy Physics.
User: lorenzovalente3
quantization-aware-training,One Bit at a Time: Impact of Quantisation on Neural Machine Translation
User: marekninja
quantization-aware-training,A Tutorial Notebook to Quantization in Machine Learning
User: maryamsoftdev
quantization-aware-training,A model compression and acceleration toolbox based on pytorch.
Organization: megvii-research
quantization-aware-training,Train neural networks with joint quantization and pruning on both weights and activations using any pytorch modules
User: mlzxy
Home Page: https://qsparse.readthedocs.io
quantization-aware-training,A lightweight Convolutional Autoencoder for recognizing Bangla font styles along with quantization for deploying resource-constrained IoT devices.
User: moshiurtonmoy
quantization-aware-training,
User: nagababumo
quantization-aware-training,EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.
User: omidghadami95
quantization-aware-training,Neural Network Compression Framework for enhanced OpenVINO™ inference
Organization: openvinotoolkit
quantization-aware-training,Classify alcohols and its snacks
Organization: project-sulsul
quantization-aware-training,Experimental Adversarial Attack notebooks on CV models
User: rishivar
quantization-aware-training,all methods of pytorch quantization based on resnet50
User: sangbumchoi
quantization-aware-training,Model Quantization with Pytorch, Tensorflow & Larq
User: satya15july
quantization-aware-training,This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.
User: sayakpaul
quantization-aware-training,Low-Precision Neural Networks for Classification on PYNQ with FINN
User: sefaburakokcu
quantization-aware-training,CNN quantization
User: sjlee94
quantization-aware-training,YOLO ModelCompression MultidatasetTraining
User: spurslipu
quantization-aware-training,Training neural nets with quantized weights on arbitrarily specified bit-depth
User: stracini-git
quantization-aware-training,Quantization for Object Detection in Tensorflow 2.x
User: tanyachutani
quantization-aware-training,针对pytorch模型的自动化模型结构分析和修改工具集,包含自动分析模型结构的模型压缩算法库
Organization: thu-mig
quantization-aware-training,Submission name: QualcommAI-EfficientNet. MicroNet Challenge (NeurIPS 2019) submission - Qualcomm AI Research
User: yashbhalgat
quantization-aware-training,3rd place solution for NeurIPS 2019 MicroNet challenge
User: yashbhalgat
quantization-aware-training,Comprehensive study on the quantization of various CNN models, employing techniques such as Post-Training Quantization and Quantization Aware Training (QAT).
User: yashmaniya0
quantization-aware-training,quantization example for pqt & qat
User: yester31
quantization-aware-training,DynamicQuantization_Bert from pytorch tutorials
User: zoetu
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