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anglenas

awesome-pruning

A curated list of neural network pruning resources.

bossnas

(ICCV 2021) BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

cream

This is a collection of our NAS and Vision Transformer work.

csnas

ctnas

[CVPR 2021] Contrastive Neural Architecture Search with Neural Architecture Comparators

easycv

An all-in-one toolkit for computer vision

efficientformer

fgd

Focal and Global Knowledge Distillation for Detectors (CVPR 2022)

fisherpruning

Group Fisher Pruning for Practical Network Compression(ICML2021)

gate-decorator-pruning

Code for the NuerIPS'19 paper "Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks"

greedy-layer-pruning

Greedy layer pruning for transformer models.

hawq

Quantization library for PyTorch. Support low-precision and mixed-precision, with hardware implementation through TVM.

hrank

Pytorch implementation of our paper accepted by CVPR 2020 (Oral) -- HRank: Filter Pruning using High-Rank Feature Map

lamcts

The release codes of LA-MCTS with its application to Neural Architecture Search.

mdistiller

A Knowledge Distillation Toolbox. The official implementation of https://arxiv.org/abs/2203.08679

ml-mobileone

This repository contains the official implementation of the research paper, "An Improved One millisecond Mobile Backbone".

mobileone

An Improved One millisecond Mobile Backbone

mobilevit-pytorch

A PyTorch implementation of "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

neuron-merging

Neuron Merging: Compensating for Pruned Neurons (NeurIPS 2020)

only_train_once

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

shufflenet-series

slimmable_networks

Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019

spvit

This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

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

taylor_pruning

Pruning Neural Networks with Taylor criterion in Pytorch

tinyml

twins

Two simple and effective designs of vision transformer, which is on par with the Swin transformer

vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch