722 Repositories
Python protocol-2day-training Libraries
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training [Arxiv] VideoMAE: Masked Autoencoders are Data-Efficient Learne
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"
Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy
"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang
SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements
✨ Real-life Data Analysis and Model Training Workshop by Global AI Hub.
🎓 Data Analysis and Model Training Course by Global AI Hub Syllabus: Day 1 What is Data? Multimedia Structured and Unstructured Data Data Types Data
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception
Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1 Liang Pan1 Zhongang Cai1,2,3 Ziwei Liu1* 1S-Lab, Nanyang Technologic
CLOOB training (JAX) and inference (JAX and PyTorch)
cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
SageMaker Studio Lab Sample Notebooks Available today in public preview. If you are looking for a no-cost compute environment to run Jupyter notebooks
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)
MetaShift: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts This repo provides the PyTorch source code of our paper: Me
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training By Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue. This
Coreference resolution for English, French, German and Polish, optimised for limited training data and easily extensible for further languages
Coreferee Author: Richard Paul Hudson, Explosion AI 1. Introduction 1.1 The basic idea 1.2 Getting started 1.2.1 English 1.2.2 French 1.2.3 German 1.2
Repository for training material for the 2022 SDSC HPC/CI User Training Course
hpc-training-2022 Repository for training material for the 2022 SDSC HPC/CI Training Series HPC/CI Training Series home https://www.sdsc.edu/event_ite
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.
English | 简体中文 Easy Parallel Library Overview Easy Parallel Library (EPL) is a general and efficient library for distributed model training. Usability
code for TCL: Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022
Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022 News (03/16/2022) upload retrieval checkpoints finetuned on COCO and Flickr T
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations
💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily install with pip.
As-ViT: Auto-scaling Vision Transformers without Training
As-ViT: Auto-scaling Vision Transformers without Training [PDF] Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou In ICLR 2
CLIP (Contrastive Language–Image Pre-training) for Italian
Italian CLIP CLIP (Radford et al., 2021) is a multimodal model that can learn to represent images and text jointly in the same space. In this project,
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools
Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t
Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Paper | Blog OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene
HTTP Protocol Stack Remote Code Execution Vulnerability CVE-2022-21907
CVE-2022-21907 Description POC for CVE-2022-21907: HTTP Protocol Stack Remote Code Execution Vulnerability. create by antx at 2022-01-17. Detail HTTP
Code of paper: "DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks"
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks Abstract: Adversarial training has been proven to
Potato Disease Classification - Training, Rest APIs, and Frontend to test.
Potato Disease Classification Setup for Python: Install Python (Setup instructions) Install Python packages pip3 install -r training/requirements.txt
N3RP (the NFT Rental Protocol) allows users to trustlessly rent out their ERC721-based assets.
N3RP • N3RP - An NFT Rental Protocol (pronounced "nerp") Smart Contracts Passing Tests, Frontend Functional But Is Being Beautified. 🛠 Introduction T
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.
TorchMultimodal (Alpha Release) Introduction TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.
PromptDet: Expand Your Detector Vocabulary with Uncurated Images
PromptDet: Expand Your Detector Vocabulary with Uncurated Images Paper Website Introduction The goal of this work is to establish a scalable pipeline
Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022
AMRBART An implementation for ACL2022 paper "Graph Pre-training for AMR Parsing and Generation". You may find our paper here (Arxiv). Requirements pyt
[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators
AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
beyond masking Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers The code is coming Figure 1: Pipeline of token-based pre-
Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)
LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale
SIGIR'22 paper: Axiomatically Regularized Pre-training for Ad hoc Search
Introduction This codebase contains source-code of the Python-based implementation (ARES) of our SIGIR 2022 paper. Chen, Jia, et al. "Axiomatically Re
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F
DeepGNN is a framework for training machine learning models on large scale graph data.
DeepGNN Overview DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features in
Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process
Aiming at the common training datsets split, spectrum preprocessing, wavelength select and calibration models algorithm involved in the spectral analysis process, a complete algorithm library is established, which is named opensa (openspectrum analysis).
Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃
This repository provides a library for efficient training of masked language models (MLM), built with fairseq. We fork fairseq to give researchers mor
Code for our SIGIR 2022 accepted paper : P3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
P3 Ranker Implementation for our SIGIR2022 accepted paper: P3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-bas
A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or simply to separate onnx files to any size you want.
sne4onnx A very simple tool for situations where optimization with onnx-simplifier would exceed the Protocol Buffers upper file size limit of 2GB, or
In this project we predict the forest cover type using the cartographic variables in the training/test datasets.
Kaggle Competition: Forest Cover Type Prediction In this project we predict the forest cover type (the predominant kind of tree cover) using the carto
Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation This is the implementaion of our paper: Bridging the
Repository for DNN training, theory to practice, part of the Large Scale Machine Learning class at Mines Paritech
DNN Training, from theory to practice This repository is complementary to the deep learning training lesson given to les Mines ParisTech on the 11th o
[CVPR 2022] "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy" by Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy Codes for this paper: [CVPR 2022] The Pr
Architecture Patterns with Python (TDD, DDD, EDM)
architecture-traning Architecture Patterns with Python (TDD, DDD, EDM) Chapter 5. 높은 기어비와 낮은 기어비의 TDD 5.2 도메인 계층 테스트를 서비스 계층으로 옮겨야 하는가? 도메인 계층 테스트 def
E2e music remastering system - End-to-end Music Remastering System Using Self-supervised and Adversarial Training
End-to-end Music Remastering System This repository includes source code and pre
Super-Fast-Adversarial-Training - A PyTorch Implementation code for developing super fast adversarial training
Super-Fast-Adversarial-Training This is a PyTorch Implementation code for develo
HashNeRF-pytorch - Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives
HashNeRF-pytorch Instant-NGP recently introduced a Multi-resolution Hash Encodin
Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Interact remotely with the computer using Python and MQTT protocol 💻
Comandos_Remotos Interagir remotamento com o computador através do Python e protocolo MQTT. 💻 Status: em desenvolvimento 🚦 Objetivo: Interagir com o
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training The Unreasonable Effectiveness of
LyaNet: A Lyapunov Framework for Training Neural ODEs
LyaNet: A Lyapunov Framework for Training Neural ODEs Provide the model type--config-name to train and test models configured as those shown in the pa
This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.
This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.
To build a regression model to predict the concrete compressive strength based on the different features in the training data.
Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features
A training task for web scraping using python multithreading and a real-time-updated list of available proxy servers.
Parallel web scraping The project is a training task for web scraping using python multithreading and a real-time-updated list of available proxy serv
Official code of Retinal Vessel Segmentation with Pixel-wise Adaptive Filters and Consistency Training
Official code of Retinal Vessel Segmentation with Pixel-wise Adaptive Filters and Consistency Training (ISBI 2022)
The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding.
SuperGen The source code for Generating Training Data with Language Models: Towards Zero-Shot Language Understanding. Requirements Before running, you
FewBit — a library for memory efficient training of large neural networks
FewBit FewBit — a library for memory efficient training of large neural networks. Its efficiency originates from storage optimizations applied to back
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy
Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve
Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"
Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that generalizes score-based models to fully nonlinear forward and backward diffusions.
This repository provides an efficient PyTorch-based library for training deep models.
An Efficient Library for Training Deep Models This repository provides an efficient PyTorch-based library for training deep models. Installation Make
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b
JstDoS - HTTP Protocol Stack Remote Code Execution Vulnerability
jstDoS If you are going to skid that, please give credits ! ^^ ¿How works? This
Data from "Datamodels: Predicting Predictions with Training Data"
Data from "Datamodels: Predicting Predictions with Training Data" Here we provid
Training a deep learning model on the noisy CIFAR dataset
Training-a-deep-learning-model-on-the-noisy-CIFAR-dataset This repository contai
Simple codebase for flexible neural net training
neural-modular Simple codebase for flexible neural net training. Allows for seamless exchange of models, dataset, and optimizers. Uses hydra for confi
This is an early in-development version of training CLIP models with hivemind.
A transformer that does not hog your GPU memory This is an early in-development codebase: if you want a stable and documented hivemind codebase, look
The NewSHead dataset is a multi-doc headline dataset used in NHNet for training a headline summarization model.
This repository contains the raw dataset used in NHNet [1] for the task of News Story Headline Generation. The code of data processing and training is available under Tensorflow Models - NHNet.
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
Training DiffWave using variational method from Variational Diffusion Models.
Variational DiffWave Training DiffWave using variational method from Variational Diffusion Models. Quick Start python train_distributed.py discrete_10
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址:https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation This the repository for this paper. Find extensions of this w
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution This code belongs to the paper [1] available at https://arx
For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.
LongScientificFormer For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training. Some code
Framework for training options with different attention mechanism and using them to solve downstream tasks.
Using Attention in HRL Framework for training options with different attention mechanism and using them to solve downstream tasks. Requirements GPU re
Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation
Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation By: Zayd Hammoudeh and Daniel Lowd Paper: Arxiv Preprint Coming soo
Post-training Quantization for Neural Networks with Provable Guarantees
Post-training Quantization for Neural Networks with Provable Guarantees Authors: Jinjie Zhang ([email protected]), Yixuan Zhou ([email protected]) and Ray
Python-slp - Side Ledger Protocol With Python
Side Ledger Protocol Run python-slp node First install Mongo DB and run the mong
SAS: Self-Augmentation Strategy for Language Model Pre-training
SAS: Self-Augmentation Strategy for Language Model Pre-training This repository
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti
Cycle Self-Training for Domain Adaptation (NeurIPS 2021)
CST Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021) Prerequisites torch=1.7.0 torchvision qpsolvers numpy prettytable tqd
Django SMTP Protocol with Gmail
Django SMTP Protocol with Gmail This is the free service from gmail to send and receive emails. What we need for this things done, Python/pip install
CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs
CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair
The repository includes the code for training cell counting applications. (Keras + Tensorflow)
cell_counting_v2 The repository includes the code for training cell counting applications. (Keras + Tensorflow) Dataset can be downloaded here : http:
Segmentation Training Pipeline
Segmentation Training Pipeline This package is a part of Musket ML framework. Reasons to use Segmentation Pipeline Segmentation Pipeline was developed
PyTorchMemTracer - Depict GPU memory footprint during DNN training of PyTorch
A Memory Tracer For PyTorch OOM is a nightmare for PyTorch users. However, most
First steps with Python in Life Sciences
First steps with Python in Life Sciences This course material is part of the "First Steps with Python in Life Science" three-day course of SIB-trainin
Collapse by Conditioning: Training Class-conditional GANs with Limited Data
Collapse by Conditioning: Training Class-conditional GANs with Limited Data Moha
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch
Repo for investigation of timeouts that happens with prolonged training on clients
Flower-timeout Repo for investigation of timeouts that happens with prolonged training on clients. This repository is meant purely for demonstration o
GitHub Actions Docker training
GitHub-Actions-Docker-training Training exercise repository for GitHub Actions using a docker base. This repository should be cloned and used for trai
Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning
Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning. Circuit Training is an open-s
X-VLM: Multi-Grained Vision Language Pre-Training
X-VLM: learning multi-grained vision language alignments Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts. Yan Zeng, Xi
ColossalAI-Examples - Examples of training models with hybrid parallelism using ColossalAI
ColossalAI-Examples This repository contains examples of training models with Co
A novel Engagement Detection with Multi-Task Training (ED-MTT) system
A novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes MSE and triplet loss together to determine the engagement level of students in an e-learning environment.
TiP-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling
TiP-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling This is the official code release for the paper 'TiP-Adapter: Training-fre
A PyTorch implementation of VIOLET
VIOLET: End-to-End Video-Language Transformers with Masked Visual-token Modeling A PyTorch implementation of VIOLET Overview VIOLET is an implementati
A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV
ffcv ImageNet Training A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get... ...high accur
A backend for mdbook in Python for generating PDF based on Chrome DevTools Protocol.
mdbook-pdf A backend for mdbook written in Python for generating PDF based on Chrome DevTools Protocol. Python library dependency Usage Put mdbook-pdf
"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices", official implementation
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices This repository contains the official PyTorch implemen
Implement of "Training deep neural networks via direct loss minimization" in PyTorch for 0-1 loss
This is the implementation of "Training deep neural networks via direct loss minimization" published at ICML 2016 in PyTorch. The implementation targe
Pianote - An application that helps musicians practice piano ear training
Pianote Pianote is an application that helps musicians practice piano ear traini
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
Monitor deep learning model training and hardware usage from mobile. 🔥 Features Monitor running experiments from mobile phone (or laptop) Monitor har
SMTP In some vulnerable configurations, email servers can also be aggregated Use information that gives us information about the host or network Give
SMTP In some vulnerable configurations, email servers can also be aggregated Use information that gives us information about the host or network Give. The SMTP protocol supports some basic commands such as VRFY and EXPN Slowly A VRFY request asks the server to verify an email address while Which requests an EXPN request for email list membership. These requests are The power is sometimes misused to verify the existence of a user on the email server Used by hackers later