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An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Awesome Knowledge Distillation
[CVPR 2023] DepGraph: Towards Any Structural Pruning; LLMs, Vision Foundation Models, etc.
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
A curated list of neural network pruning resources.
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
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.
A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
Pytorch implementation of various Knowledge Distillation (KD) methods.
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
Efficient computing methods developed by Huawei Noah's Ark Lab
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
Collection of recent methods on (deep) neural network compression and acceleration.
[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.