126 Open source projects that are alternatives of or similar to Model_compression

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

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

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

reference pytorch code for named entity tagging

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

Infrastructures™ for Machine Learning Training/Inference in Production.

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

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint

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

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

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.

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

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

Embedded and mobile deep learning research resources

Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

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

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

Pruning and quantization for SSD. Model compression.

libimagequant (pngquant) bindings for the Rust language

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

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.

A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]

Collections of model quantization algorithms

Dice.com repo to accompany the dice.com 'Vectors in Search' talk by Simon Hughes, from the Activate 2018 search conference, and the 'Searching with Vectors' talk from Haystack 2019 (US). Builds upon my conceptual search and semantic search work from 2015

A treasure chest for image classification powered by PaddlePaddle

Pruning and other network surgery for trained TF.Keras models.

Powerful, automated analysis and design of quantum microwave chips & devices [Energy-Participation Ratio and more]

PyTorch implementation of Data Free Quantization Through Weight Equalization and Bias Correction.

An Open-Source Package for Deep Learning to Hash (DeepHash)

Repository to track the progress in model compression and acceleration

Brevitas: quantization-aware training in PyTorch

This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.

Fast inference engine for OpenNMT models

Palette quantization library that powers pngquant and other PNG optimizers

pytorch implementation of "Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks"

Lossy PNG compressor — pngquant command based on libimagequant library

[NeurIPS 2020] Code release for paper "Deep Multimodal Fusion by Channel Exchanging" (In PyTorch)

Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH

A PyTorch implementation of "Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights"

An Open Source Deep Learning Inference Engine Based on FPGA

Pruning and other network surgery for trained Keras models.

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

yolov3 network slimming剪枝的一种实现

Training & Quantization of embedded friendly Deep Learning / Machine Learning / Computer Vision models

Must-read papers on deep learning to hash (DeepHash)

YOLO ModelCompression MultidatasetTraining

Filter Grafting for Deep Neural Networks(CVPR 2020)

PyTorch and Keras model training and layer saturation monitor

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Dataflow compiler for QNN inference on FPGAs

A curated list of neural network pruning resources.

QKeras: a quantization deep learning library for Tensorflow Keras

One Person, One Model, One World: Learning Continual User Representation without Forgetting

Graph Transforms to Quantize and Retrain Deep Neural Nets in TensorFlow

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

Simulate quantization and quantization aware training for MXNet-Gluon models.

A PyTorch-based model pruning toolkit for pre-trained language models

[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

🎨 Simple color palette quantization using MMCQ