846 Repositories
Python architecture-training Libraries
A light weight data augmentation tool for training CNNs and Viola Jones detectors
hey-daug A light weight data augmentation tool for training CNNs and Viola Jones detectors (Haar Cascades). This tool inflates your data by up to six
Tools for curating biomedical training data for large-scale language modeling
Tools for curating biomedical training data for large-scale language modeling
Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks.
The Lottery Ticket Hypothesis for Pre-trained BERT Networks Code for this paper The Lottery Ticket Hypothesis for Pre-trained BERT Networks. [NeurIPS
Source code for "Efficient Training of BERT by Progressively Stacking"
Introduction This repository is the code to reproduce the result of Efficient Training of BERT by Progressively Stacking. The code is based on Fairseq
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
ELECTRA Introduction ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using
Understanding the Difficulty of Training Transformers
Admin Understanding the Difficulty of Training Transformers Guided by our analyses, we propose Adaptive Model Initialization (Admin), which successful
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
DeepSpeed+Megatron trained the world's most powerful language model: MT-530B DeepSpeed is hiring, come join us! DeepSpeed is a deep learning optimizat
[ACL-IJCNLP 2021] "EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets"
EarlyBERT This is the official implementation for the paper in ACL-IJCNLP 2021 "EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets" by
Training and evaluation codes for the BertGen paper (ACL-IJCNLP 2021)
BERTGEN This repository is the implementation of the paper "BERTGEN: Multi-task Generation through BERT" (https://arxiv.org/abs/2106.03484). The codeb
Revisiting Self-Training for Few-Shot Learning of Language Model.
SFLM This is the implementation of the paper Revisiting Self-Training for Few-Shot Learning of Language Model. SFLM is short for self-training for few
[EMNLP 2021] Improving and Simplifying Pattern Exploiting Training
ADAPET This repository contains the official code for the paper: "Improving and Simplifying Pattern Exploiting Training". The model improves and simpl
The code is the training example of AAAI2022 Security AI Challenger Program Phase 8: Data Centric Robot Learning on ML models.
Example code of [Tianchi AAAI2022 Security AI Challenger Program Phase 8]
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [pdf] The official repository for Self-Supervised Pre-Training for Transfo
Efficient training of deep recommenders on cloud.
HybridBackend Introduction HybridBackend is a training framework for deep recommenders which bridges the gap between evolving cloud infrastructure and
Quantity Takeoff with Python. Collecting groups of elements by filters
The free tool QuantityTakeoff allows you to group elements from Revit and IFC models (in BIMJSON-CSV format) with just a few filters and find the required volume values for the grouped elements.
Agile Threat Modeling Toolkit
Threagile is an open-source toolkit for agile threat modeling:
Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation [Arxiv] [Paper] As acquiring pixel-wise an
Code for "Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis
HAABSAStar Code for "Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis". This project builds on the code from https://gith
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️⚠️
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples This repository is the official implementation of "Tow
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback
Online Learning Of Neural Computations From Sparse Temporal Feedback This repository is the official implementation of the NeurIPS 2021 paper Online L
Official implementation for TTT++: When Does Self-supervised Test-time Training Fail or Thrive
TTT++ This is an official implementation for TTT++: When Does Self-supervised Test-time Training Fail or Thrive? TL;DR: Online Feature Alignment + Str
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models This repository is the official implementation of the fol
A paper list of pre-trained language models (PLMs).
Large-scale pre-trained language models (PLMs) such as BERT and GPT have achieved great success and become a milestone in NLP.
Minimalistic PyTorch training loop
Backbone for PyTorch training loop Will try to keep it minimalistic. pip install back from back import Bone Features Progress bar Checkpoints saving/l
Code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search.
TransNAS-Bench-101 This repository contains the publishable code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizabili
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository provides the official PyTorch implementation
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"
CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2
The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".
FQ-ViT [arXiv] This repo contains the official implementation of "FQ-ViT: Fully Quantized Vision Transformer without Retraining". Table of Contents In
Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training"
Saliency Guided Training Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training" by Aya Abdelsalam Ismail, Hector Cor
Pre-Training 3D Point Cloud Transformers with Masked Point Modeling
Point-BERT: Pre-Training 3D Point Cloud Transformers with Masked Point Modeling Created by Xumin Yu*, Lulu Tang*, Yongming Rao*, Tiejun Huang, Jie Zho
League of Legends Reinforcement Learning Environment (LoLRLE) multiple training scenarios using PPO.
League of Legends Reinforcement Learning Environment (LoLRLE) About This repo contains code to train an agent to play league of legends in a distribut
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics. By Andres Milioto @ University of Bonn. (for the new P
PyTorch common framework to accelerate network implementation, training and validation
pytorch-framework PyTorch common framework to accelerate network implementation, training and validation. This framework is inspired by works from MML
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"
CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2
Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust.
Subspace Adversarial Training Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However,
ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation
ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation (Accepted by BMVC'21) Abstract: Images acquir
Codes for paper "KNAS: Green Neural Architecture Search"
KNAS Codes for paper "KNAS: Green Neural Architecture Search" KNAS is a green (energy-efficient) Neural Architecture Search (NAS) approach. It contain
Mixing up the Invariant Information clustering architecture, with self supervised concepts from SimCLR and MoCo approaches
Self Supervised clusterer Combined IIC, and Moco architectures, with some SimCLR notions, to get state of the art unsupervised clustering while retain
This repo contains simple to use, pretrained/training-less models for speaker diarization.
PyDiar This repo contains simple to use, pretrained/training-less models for speaker diarization. Supported Models Binary Key Speaker Modeling Based o
Source code for CsiNet and CRNet using Fully Connected Layer-Shared feedback architecture.
FCS-applications Source code for CsiNet and CRNet using the Fully Connected Layer-Shared feedback architecture. Introduction This repository contains
Large scale and asynchronous Hyperparameter Optimization at your fingertip.
Syne Tune This package provides state-of-the-art distributed hyperparameter optimizers (HPO) where trials can be evaluated with several backend option
A procedural Blender pipeline for photorealistic training image generation
BlenderProc2 A procedural Blender pipeline for photorealistic rendering. Documentation | Tutorials | Examples | ArXiv paper | Workshop paper Features
Training open neural machine translation models
Train Opus-MT models This package includes scripts for training NMT models using MarianNMT and OPUS data for OPUS-MT. More details are given in the Ma
High performance distributed framework for training deep learning recommendation models based on PyTorch.
PERSIA (Parallel rEcommendation tRaining System with hybrId Acceleration) is developed by AI platform@Kuaishou Technology, collaborating with ETH. It
Automatically download the cwru data set, and then divide it into training data set and test data set
Automatically download the cwru data set, and then divide it into training data set and test data set.自动下载cwru数据集,然后分训练数据集和测试数据集
Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.
clip-text-decoder Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script. Example Predi
Make differentially private training of transformers easy for everyone
private-transformers This codebase facilitates fast experimentation of differentially private training of Hugging Face transformers. What is this? Why
Pytorch library for end-to-end transformer models training and serving
Pytorch library for end-to-end transformer models training and serving
Problem statements on System Design and Software Architecture as part of Arpit's System Design Masterclass
Problem statements on System Design and Software Architecture as part of Arpit's System Design Masterclass
Code accompanying paper: Meta-Learning to Improve Pre-Training
Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers
YOLOv4-v3 Training Automation API for Linux
This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset or label your dataset using our BMW-LabelTool-Lite and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. NoCode training with YOLOv4 and YOLOV3 has never been so easy.
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Intel(R) Extension for Scikit-learn* Installation | Documentation | Examples | Support | FAQ With Intel(R) Extension for Scikit-learn you can accelera
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is
This is a simple Tic-Tac-Toe game.
Tic-Tac-Toe Nosso famoso e tradicional Jogo da Velha, mas agora em Python. Development setup Para rodar o programa, basta instalar python em sua maqui
Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper.
Self-Supervised Policy Adaptation during Deployment PyTorch implementation of PAD and evaluation benchmarks from Self-Supervised Policy Adaptation dur
AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch
AutoML for Image Semantic Segmentation Currently this repo contains the only working open-source implementation of Auto-Deeplab which, by the way out-
PySpark bindings for H3, a hierarchical hexagonal geospatial indexing system
h3-pyspark: Uber's H3 Hexagonal Hierarchical Geospatial Indexing System in PySpark PySpark bindings for the H3 core library. For available functions,
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [pdf] The official repository for Self-Supervised Pre-Training for Transfo
A colab notebook for training Stylegan2-ada on colab, transfer learning onto your own dataset.
Stylegan2-Ada-Google-Colab-Starter-Notebook A no thrills colab notebook for training Stylegan2-ada on colab. transfer learning onto your own dataset h
Code to reprudece NeurIPS paper: Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
Accelerated Sparse Neural Training: A Provable and Efficient Method to FindN:M Transposable Masks Recently, researchers proposed pruning deep neural n
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.
fastai ulmfit - Pretraining the Language Model, Fine-Tuning and training a Classifier
fast.ai ULMFiT with SentencePiece from pretraining to deployment Motivation: Why even bother with a non-BERT / Transformer language model? Short answe
An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
Welcome to AdaptNLP A high level framework and library for running, training, and deploying state-of-the-art Natural Language Processing (NLP) models
An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come
An Agnostic Object Detection Framework IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-q
This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".
A Memory-saving Training Framework for Transformers This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for Trans
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear
Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
livelossplot Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training! (RECENT CHANGES, EXAMPLES IN COLAB, A
This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".
Mesa: A Memory-saving Training Framework for Transformers This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for
Naszilla is a Python library for neural architecture search (NAS)
A repository to compare many popular NAS algorithms seamlessly across three popular benchmarks (NASBench 101, 201, and 301). You can implement your ow
The pure and clear PyTorch Distributed Training Framework.
The pure and clear PyTorch Distributed Training Framework. Introduction Requirements and Usage Dependency Dataset Basic Usage Slurm Cluster Usage Base
Scheme for training and applying a label propagation framework
Factorisation-based Image Labelling Overview This is a scheme for training and applying the factorisation-based image labelling (FIL) framework. Some
A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset
YOLOv4 CrowdHuman Tutorial This is a tutorial demonstrating how to train a YOLOv4 people detector using Darknet and the CrowdHuman dataset. Table of c
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).
AutoML library for deep learning
Official Website: autokeras.com AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras
Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.
HiddenLayer A lightweight library for neural network graphs and training metrics for PyTorch, Tensorflow, and Keras. HiddenLayer is simple, easy to ex
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
SageMaker Python SDK SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the S
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of
Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness (NeurIPS2021) This repository contains code for the paper "Smo
Official PyTorch implementation for "Low Precision Decentralized Distributed Training with Heterogenous Data"
Low Precision Decentralized Training with Heterogenous Data Official PyTorch implementation for "Low Precision Decentralized Distributed Training with
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).
Fisher Induced Sparse uncHanging (FISH) Mask This repo contains the code for Fisher Induced Sparse uncHanging (FISH) Mask training, from "Training Neu
Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)
DocFormer - PyTorch Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for t
Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
UTNet (Accepted at MICCAI 2021) Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation Introduction Transf
Simulation code and tutorial for BBHnet training data
Simulation Dataset for BBHnet NOTE: OLD README, UPDATE IN PROGRESS We generate simulation dataset to train BBHnet, our deep learning framework for det
Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++ [ICCV-2021]. Overview This package contains the model implementation and training
PyTorch Kafka Dataset: A definition of a dataset to get training data from Kafka.
PyTorch Kafka Dataset: A definition of a dataset to get training data from Kafka.
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T
RP-GAN: Stable GAN Training with Random Projections
RP-GAN: Stable GAN Training with Random Projections This repository contains a reference implementation of the algorithm described in the paper: Behna
Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"
Language Generation with Recurrent Generative Adversarial Networks without Pre-training Code for training and evaluation of the model from "Language G
A stable algorithm for GAN training
DRAGAN (Deep Regret Analytic Generative Adversarial Networks) Link to our paper - https://arxiv.org/abs/1705.07215 Pytorch implementation (thanks!) -
Code for ICLR2018 paper: Improving GAN Training via Binarized Representation Entropy (BRE) Regularization - Y. Cao · W Ding · Y.C. Lui · R. Huang
code for "Improving GAN Training via Binarized Representation Entropy (BRE) Regularization" (ICLR2018 paper) paper: https://arxiv.org/abs/1805.03644 G
Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics
Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis This repository contains all the code used in L. de Oliveira (@lukedeo),
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"
Simulated+Unsupervised (S+U) Learning in TensorFlow TensorFlow implementation of Learning from Simulated and Unsupervised Images through Adversarial T
Code for the paper "Improved Techniques for Training GANs"
Status: Archive (code is provided as-is, no updates expected) improved-gan code for the paper "Improved Techniques for Training GANs" MNIST, SVHN, CIF
High performance distributed framework for training deep learning recommendation models based on PyTorch.
High performance distributed framework for training deep learning recommendation models based on PyTorch.
CCQA A New Web-Scale Question Answering Dataset for Model Pre-Training
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training This is the official repository for the code and models of the paper CCQA: A N
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi