351 Open source projects that are alternatives of or similar to regnet.pytorch

A tensorflow2 implementation of some basic CNNs(MobileNetV1/V2/V3, EfficientNet, ResNeXt, InceptionV4, InceptionResNetV1/V2, SENet, SqueezeNet, DenseNet, ShuffleNetV2, ResNet).

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Awesome Neural Architecture Search Papers

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Naszilla is a Python library for neural architecture search (NAS)

A lightweight and easy-to-use deep learning framework with neural architecture search.

NSGA-Net, a Neural Architecture Search Algorithm

aw_nas: A Modularized and Extensible NAS Framework

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

DeepArchitect: Automatically Designing and Training Deep Architectures

Bridging the gap Between Stability and Scalability in Neural Architecture Search

SGAS: Sequential Greedy Architecture Search (CVPR'2020) https://www.deepgcns.org/auto/sgas

Single Path One-Shot NAS MXNet implementation with full training and searching pipeline. Support both Block and Channel Selection. Searched models better than the original paper are provided.

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

Code for Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells, CVPR '19

"NAS evaluation is frustratingly hard", ICLR2020

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

AutoML frAmework for Neural Networks

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

A general, modular, and programmable architecture search framework

This directory contains code necessary to run the GraphNAS algorithm.

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

[CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

Multi-Task Learning Framework on PyTorch. State-of-the-art methods are implemented to effectively train models on multiple tasks.

Automated deep learning algorithms implemented in PyTorch.

[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

[CVPR 2020] MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

A curated list of awesome architecture search resources

[ECCV2020] NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search

AutoML library for deep learning

Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search https://arxiv.org/abs/1807.06906

Minimal Tensorflow implementation of the paper "Neural Architecture Search With Reinforcement Learning" presented at ICLR 2017

Fast & Simple Resource-Constrained Learning of Deep Network Structure

Genetic neural architecture search with Keras

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

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.

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

Federated Learning Library: https://fedml.ai

a distributed Hyperband implementation on Steroids

[ICLR 2020] "FasterSeg: Searching for Faster Real-time Semantic Segmentation" by Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang

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

Basic implementation of [Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578).

Fast and flexible AutoML with learning guarantees.

Differentiable architecture search for convolutional and recurrent networks

real-time network architecture for mobile devices and semantic segmentation

AutoGluon: AutoML for Text, Image, and Tabular Data

A curated list of automated machine learning papers, articles, tutorials, slides and projects

Reproducible Rapid Research for Neural Architecture Search (NAS)

Neural Architecture Search Powered by Swarm Intelligence 🐜

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