80 Open source projects that are alternatives of or similar to Inq Pytorch

Go library for color quantization and dithering

PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.

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.

Dataflow compiler for QNN inference on FPGAs

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

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

Repository containing code for "U-Net Fixed-Point Quantization for Medical Image Segmentation" paper at MICCAI2019

FrostNet: Towards Quantization-Aware Network Architecture Search

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

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

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

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

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

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

The implementation of AAAI-17 paper "Collective Deep Quantization of Efficient Cross-modal Retrieval"

Trainable models and NN optimization tools

TensorFlow Quantization Example, for TensorFlow Lite

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

理杏仁开发平台python api(非官方)

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]

All about acceleration and compression of Deep Neural Networks

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

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.

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

Palette quantization library that powers pngquant and other PNG optimizers

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

reference pytorch code for named entity tagging

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

An Open Source Deep Learning Inference Engine Based on FPGA

QKeras: a quantization deep learning library for Tensorflow Keras

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

🎨 Simple color palette quantization using MMCQ

Graph Transforms to Quantize and Retrain Deep Neural Nets in TensorFlow

Pruning and quantization for SSD. Model compression.

SDK for TEE AI Stick (includes model training script, inference library, examples)

KGy SOFT Drawing is a library for advanced image, icon and graphics handling.

Reducing the size of convolutional neural networks

🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

libimagequant (pngquant) bindings for the Rust language

Autoencoder based image compression: can the learning be quantization independent? https://arxiv.org/abs/1802.09371

Fast inference engine for OpenNMT models

A collection of SOTA Image Classification Models in PyTorch

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

Compact high quality word embeddings for Russian language

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

Library used to deal with colors and images. You can extract colors from images.

A treasure chest for image classification powered by PaddlePaddle

A Python Wrapper of Pngquant

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

Training Low-bits DNNs with Stochastic Quantization

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

State-of-the-art method for large-scale ANN search as of Oct 2016. Presented at ECCV 16.

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

Lossy PNG compressor — pngquant command based on libimagequant library

Quantization of Convolutional Neural networks.

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

Collections of model quantization algorithms

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

Brevitas: quantization-aware training in PyTorch