121 Open source projects that are alternatives of or similar to SViTE

Infrastructures™ for Machine Learning Training/Inference in Production.

Awesome machine learning model compression research papers, tools, and learning material.

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

A pytorch pruning toolkit for structured neural network pruning and layer dependency maintaining.

A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

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

(CVPR 2021, Oral) Dynamic Slimmable Network

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"

A curated list of neural network pruning resources.

PaddleSlim is an open-source library for deep model compression and architecture search.

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

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 package to make do Network Slimming a little easier

This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"

Implement of Dynamic Model Pruning with Feedback with pytorch

BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

Remove/Backup – settings & cli for macOS (OS X) – DataGrip, AppCode, CLion, Gogland, IntelliJ, PhpStorm, PyCharm, Rider, RubyMine, WebStorm

[ICLR 2021] "GANs Can Play Lottery Too" by Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen

Prunable nn layers for pytorch.

PyTorch implementation of "Pruning Filters For Efficient ConvNets"

CVPR 2021 : Zero-shot Adversarial Quantization (ZAQ)

Automatic DNN compression tool with various model compression and neural architecture search techniques

Yolov5 distillation training | Yolov5知识蒸馏训练，支持训练自己的数据

Unofficial Pytorch implementation of Deep Compression in CIFAR10

Learnable Embedding Space for Efficient Neural Architecture Compression

Library - Vanilla, ViT, DeiT, BERT, GPT

Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool), targeting to provide unified APIs for network compression technologies, such as low precision quantization, sparsity, pruning, knowledge distillation, across different deep learning frameworks to pursue optimal inference performance.

[NeurIPS 2021] DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

🔌⚡ Nuxt module to prune html before sending it to the browser (it removes elements matching CSS selector(s)), useful for boosting performance showing a different HTML for bots/audits by removing all the scripts with dynamic rendering

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

Your local Flux surgeon

A research library for pytorch-based neural network pruning, compression, and more.

Efficient-Computing

Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

Official PyTorch Implementation of Long-Short Transformer (NeurIPS 2021).

Attempting feature map prunning on a CNN trained for Japanese OCR

A PyTorch implementation of the iterative pruning method described in Han et. al. (2015)

🤗🖼️ HuggingPics: Fine-tune Vision Transformers for anything using images found on the web.

It is a blueprint to data science from the mathematics to algorithms. It is not completed.

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

EsViT: Efficient self-supervised Vision Transformers

Caffe for Sparse Convolutional Neural Network

PyTorch*-based Neural Network Compression Framework for enhanced OpenVINO™ inference

pytorch realtime multi person keypoint estimation

Observations and notes to understand the workings of neural network models and other thought experiments using Tensorflow

Pruning and quantization for SSD. Model compression.

[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！

pytorch implementation of Structured Bayesian Pruning

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

This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.

OpenMMLab Model Compression Toolbox and Benchmark.

Official PyTorch implementation of our ECCV 2022 paper "Sliced Recursive Transformer"

PyTorch Model Compression

A curated list of awesome edge machine learning resources, including research papers, inference engines, challenges, books, meetups and others.