Channel PruningChannel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
Stars: ✭ 979 (+515.72%)
Soft Filter PruningSoft Filter Pruning for Accelerating Deep Convolutional Neural Networks
Stars: ✭ 291 (+83.02%)
Torch PruningA pytorch pruning toolkit for structured neural network pruning and layer dependency maintaining.
Stars: ✭ 193 (+21.38%)
Awesome PruningA curated list of neural network pruning resources.
Stars: ✭ 1,017 (+539.62%)
ESNACLearnable Embedding Space for Efficient Neural Architecture Compression
Stars: ✭ 27 (-83.02%)
HawqQuantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.
Stars: ✭ 108 (-32.08%)
DS-Net(CVPR 2021, Oral) Dynamic Slimmable Network
Stars: ✭ 204 (+28.3%)
LightctrLightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
Stars: ✭ 644 (+305.03%)
Amc[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Stars: ✭ 298 (+87.42%)
Keras model compressionModel Compression Based on Geoffery Hinton's Logit Regression Method in Keras applied to MNIST 16x compression over 0.95 percent accuracy.An Implementation of "Distilling the Knowledge in a Neural Network - Geoffery Hinton et. al"
Stars: ✭ 59 (-62.89%)
allie🤖 A machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers).
Stars: ✭ 93 (-41.51%)
Model OptimizationA toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Stars: ✭ 992 (+523.9%)
FastPosepytorch realtime multi person keypoint estimation
Stars: ✭ 36 (-77.36%)
Collaborative DistillationPyTorch code for our CVPR'20 paper "Collaborative Distillation for Ultra-Resolution Universal Style Transfer"
Stars: ✭ 138 (-13.21%)
ZAQ-codeCVPR 2021 : Zero-shot Adversarial Quantization (ZAQ)
Stars: ✭ 59 (-62.89%)
Awesome Automl And Lightweight ModelsA 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.
Stars: ✭ 691 (+334.59%)
NniAn open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Stars: ✭ 10,698 (+6628.3%)
Mobile IdDeep Face Model Compression
Stars: ✭ 187 (+17.61%)
Ghostnet.pytorch[CVPR2020] GhostNet: More Features from Cheap Operations
Stars: ✭ 440 (+176.73%)
Filter Pruning Geometric MedianFilter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)
Stars: ✭ 338 (+112.58%)
AquvitaeThe Easiest Knowledge Distillation Library for Lightweight Deep Learning
Stars: ✭ 71 (-55.35%)
MicroexpnetMicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images
Stars: ✭ 121 (-23.9%)
SViTE[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
Stars: ✭ 50 (-68.55%)
DLCV2018SPRINGDeep Learning for Computer Vision (CommE 5052) in NTU
Stars: ✭ 38 (-76.1%)
Yolov3yolov3 by pytorch
Stars: ✭ 142 (-10.69%)
CompressCompressing Representations for Self-Supervised Learning
Stars: ✭ 43 (-72.96%)
Regularization-Pruning[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"
Stars: ✭ 44 (-72.33%)
Tf2An Open Source Deep Learning Inference Engine Based on FPGA
Stars: ✭ 113 (-28.93%)
ATMC[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”
Stars: ✭ 41 (-74.21%)
Knowledge Distillation PytorchA PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
Stars: ✭ 986 (+520.13%)
Amc Models[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Stars: ✭ 154 (-3.14%)
BitPackBitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.
Stars: ✭ 36 (-77.36%)
BipointnetThis project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.
Stars: ✭ 27 (-83.02%)
Auto-CompressionAutomatic DNN compression tool with various model compression and neural architecture search techniques
Stars: ✭ 19 (-88.05%)
GhostnetCV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.
Stars: ✭ 1,744 (+996.86%)
PocketflowAn Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
Stars: ✭ 2,672 (+1580.5%)
PaddleslimPaddleSlim is an open-source library for deep model compression and architecture search.
Stars: ✭ 677 (+325.79%)
Bert Of Theseus⛵️The official PyTorch implementation for "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing" (EMNLP 2020).
Stars: ✭ 209 (+31.45%)
CondensaProgrammable Neural Network Compression
Stars: ✭ 129 (-18.87%)
JfasttextJava interface for fastText
Stars: ✭ 193 (+21.38%)
Kd libA Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.
Stars: ✭ 173 (+8.81%)
NeuronblocksNLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
Stars: ✭ 1,356 (+752.83%)
Ld NetEfficient Contextualized Representation: Language Model Pruning for Sequence Labeling
Stars: ✭ 148 (-6.92%)
Pretrained Language ModelPretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
Stars: ✭ 2,033 (+1178.62%)
Micronetmicronet, 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
Stars: ✭ 1,232 (+674.84%)