291 Open source projects that are alternatives of or similar to Paddleslim

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

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

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

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

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

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

Infrastructures™ for Machine Learning Training/Inference in Production.

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

A curated list of neural network pruning resources.

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

An Open Source Deep Learning Inference Engine Based on FPGA

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

Pruning and quantization for SSD. Model compression.

reference pytorch code for named entity tagging

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

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

PyTorch Model Compression

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.

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

Automated deep learning algorithms implemented in PyTorch.

Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

Reproducible Rapid Research for Neural Architecture Search (NAS)

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

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

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

TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search (ECCV2020)

🛠️ Tools for Transformers compression using PyTorch Lightning ⚡

This repo is about NAS

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

my own deep learning mastery roadmap

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

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

CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

OpenMMLab Model Compression Toolbox and Benchmark.

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

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

"NAS evaluation is frustratingly hard", ICLR2020

[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

Awesome Neural Architecture Search Papers

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.

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

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

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

CVPR 2021 : Zero-shot Adversarial Quantization (ZAQ)

(CVPR 2021, Oral) Dynamic Slimmable Network

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Embedded and mobile deep learning research resources

Armbian configuration utility

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

real-time network architecture for mobile devices and semantic segmentation

PyTorch implementation of PNASNet-5 on ImageNet

NAS-Bench-201 API and Instruction

[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang

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

Grab your files periodically from a remote FTP or SFTP server easily

Lossy PNG compressor — pngquant command based on libimagequant library

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices