1425 Repositories
Python genome-scale-models Libraries
Unofficial JAX implementations of Deep Learning models
JAX Models Table of Contents About The Project Getting Started Prerequisites Installation Usage Contributing License Contact About The Project The JAX
This is the official implementation of our proposed SwinMR
SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi
Public Models considered for emotion estimation from EEG
Emotion-EEG Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with
(3DV 2021 Oral) Filtering by Cluster Consistency for Large-Scale Multi-Image Matching
Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching (3DV 2021 Oral Presentation) Filtering by Cluster Consistency (FCC) is a very
This repo is the official implementation for Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
1 MAGNN This repo is the official implementation for Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting. 1.1 The frame
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Awesome production machine learning This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, versi
Simple Tensorflow implementation of Toward Spatially Unbiased Generative Models (ICCV 2021)
Spatial unbiased GANs — Simple TensorFlow Implementation [Paper] : Toward Spatially Unbiased Generative Models (ICCV 2021) Abstract Recent image gener
Pytorch implementation of MLP-Mixer with loading pre-trained models.
MLP-Mixer-Pytorch PyTorch implementation of MLP-Mixer: An all-MLP Architecture for Vision with the function of loading official ImageNet pre-trained p
Generic Foreground Segmentation in Images
Pixel Objectness The following repository contains pretrained model for pixel objectness. Please visit our project page for the paper and visual resul
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.
Using the provided dataset which includes various book features, in order to predict the price of books, using various proposed methods and models.
I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive
I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive. Obstacles like sentence negation, sarcasm, terseness, language ambiguity, and many others make this task very challenging.
World Models with TensorFlow 2
World Models This repo reproduces the original implementation of World Models. This implementation uses TensorFlow 2.2. Docker The easiest way to hand
Additional code for Stable-baselines3 to load and upload models from the Hub.
Hugging Face x Stable-baselines3 A library to load and upload Stable-baselines3 models from the Hub. Installation With pip Examples [Todo: add colab t
This repo includes some graph-based CTR prediction models and other representative baselines.
Graph-based CTR prediction This is a repository designed for graph-based CTR prediction methods, it includes our graph-based CTR prediction methods: F
Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)
GANVAS-models This is an implementation of various generative models. It contains implementations of the following: Autoregressive Models: PixelCNN, G
Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver
Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver, the wheel size, gear shifting sequence by modeling drivetrain constraints to achieve maximum laps in a race with a 2-hour time window.
Keras code and weights files for popular deep learning models.
Trained image classification models for Keras THIS REPOSITORY IS DEPRECATED. USE THE MODULE keras.applications INSTEAD. Pull requests will not be revi
Face Depixelizer based on "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models" repository.
NOTE We have noticed a lot of concern that PULSE will be used to identify individuals whose faces have been blurred out. We want to emphasize that thi
A clean, easy to scale discord bot template
A clean, easy to scale discord bot template. Develope using nextcord library and can be use with any other discord.py forked library.
DCM is a set of tools that helps you to keep your data in your Django Models consistent.
Django Consistency Model DCM is a set of tools that helps you to keep your data in your Django Models consistent. Motivation You have a lot of legacy
A Transformer Implementation that is easy to understand and customizable.
Simple Transformer I've written a series of articles on the transformer architecture and language models on Medium. This repository contains an implem
Interpolation-based reduced-order models
Interpolation-reduced-order-models Interpolation-based reduced-order models High-fidelity computational fluid dynamics (CFD) solutions are time consum
Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis
Introduction This is an implementation of our paper Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis.
Reference models and tools for Cloud TPUs.
Cloud TPUs This repository is a collection of reference models and tools used with Cloud TPUs. The fastest way to get started training a model on a Cl
This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.
optimaladj: A library for computing optimal adjustment sets in causal graphical models This package implements the algorithms introduced in Smucler, S
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s
Large scale PTM - PPI relation extraction
Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT The silver standard
for a paper about leveraging discourse markers for training new models
TSLM-DISCOURSE-MARKERS Scope This repository contains: (1) Code to extract discourse markers from wikipedia (TSA). (1) Code to extract significant dis
HuSpaCy: industrial-strength Hungarian natural language processing
HuSpaCy: Industrial-strength Hungarian NLP HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing faciliti
Repository for the AugmentedPCA Python package.
Overview This Python package provides implementations of Augmented Principal Component Analysis (AugmentedPCA) - a family of linear factor models that
Specification language for generating Generalized Linear Models (with or without mixed effects) from conceptual models
tisane Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships TL;DR: Analysts can use Tisane to author gener
A framework for multi-step probabilistic time-series/demand forecasting models
JointDemandForecasting.py A framework for multi-step probabilistic time-series/demand forecasting models File stucture JointDemandForecasting contains
Exploration of BERT-based models on twitter sentiment classifications
twitter-sentiment-analysis Explore the relationship between twitter sentiment of Tesla and its stock price/return. Explore the effect of different BER
PyTorch implementation(s) of various ResNet models from Twitch streams.
pytorch-resnet-twitch PyTorch implementation(s) of various ResNet models from Twitch streams. Status: ResNet50 currently not working. Will update in n
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)
Machine learning and Deep learning models, deploy on telegram (the best social media)
Semi Intelligent BOT The project involves : Classifying fake news Classifying objects such as aeroplane, automobile, bird, cat, deer, dog, frog, horse
Script and models for clustering LAION-400m CLIP embeddings.
clustering-laion400m Script and models for clustering LAION-400m CLIP embeddings. Models were fit on the first million or so image embeddings. A subje
An executor that wraps 3D mesh models and encodes 3D content documents to d-dimension vector.
3D Mesh Encoder An Executor that receives Documents containing point sets data in its blob attribute, with shape (N, 3) and encodes it to embeddings o
Natural Language Processing Best Practices & Examples
NLP Best Practices In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive bus
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
DeepCTR DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 Tensorflow 2.0
NLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. Tab
Implementation of deep learning models for time series in PyTorch.
List of Implementations: Currently, the reimplementation of the DeepAR paper(DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection
Deep learning for time series forecasting Flow forecast is an open-source deep learning for time series forecasting framework. It provides all the lat
An API-first distributed deployment system of deep learning models using timeseries data to analyze and predict systems behaviour
Gordo Building thousands of models with timeseries data to monitor systems. Table of content About Examples Install Uninstall Developer manual How to
Forecast dynamically at scale with this unique package. pip install scalecast
🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels
Cherche (search in French) allows you to create a neural search pipeline using retrievers and pre-trained language models as rankers.
Cherche (search in French) allows you to create a neural search pipeline using retrievers and pre-trained language models as rankers. Cherche is meant to be used with small to medium sized corpora. Cherche's main strength is its ability to build diverse and end-to-end pipelines.
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort
List some popular DeepFake models e.g. DeepFake, FaceSwap-MarekKowal, IPGAN, FaceShifter, FaceSwap-Nirkin, FSGAN, SimSwap, CihaNet, etc.
deepfake-models List some popular DeepFake models e.g. DeepFake, CihaNet, SimSwap, FaceSwap-MarekKowal, IPGAN, FaceShifter, FaceSwap-Nirkin, FSGAN, Si
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
CheckList This repository contains code for testing NLP Models as described in the following paper: Beyond Accuracy: Behavioral Testing of NLP models
🔅 Shapash makes Machine Learning models transparent and understandable by everyone
🎉 What's new ? Version New Feature Description Tutorial 1.6.x Explainability Quality Metrics To help increase confidence in explainability methods, y
List of papers, code and experiments using deep learning for time series forecasting
Deep Learning Time Series Forecasting List of state of the art papers focus on deep learning and resources, code and experiments using deep learning f
Not Suitable for Work (NSFW) classification using deep neural network Caffe models.
Open nsfw model This repo contains code for running Not Suitable for Work (NSFW) classification deep neural network Caffe models. Please refer our blo
Largest list of models for Core ML (for iOS 11+)
Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation We'v
Train Scene Graph Generation for Visual Genome and GQA in PyTorch = 1.2 with improved zero and few-shot generalization.
Scene Graph Generation Object Detections Ground truth Scene Graph Generated Scene Graph In this visualization, woman sitting on rock is a zero-shot tr
CMS for everyone, easy to deploy and scale, robust modular system with many packages.
Django-Leonardo Full featured platform for fast and easy building extensible web applications. Don't waste your time searching stable solution for dai
Simulate genealogical trees and genomic sequence data using population genetic models
msprime msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consis
A Python Package For System Identification Using NARMAX Models
SysIdentPy is a Python module for System Identification using NARMAX models built on top of numpy and is distributed under the 3-Clause BSD license. N
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models.
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
bottom-up-attention This code implements a bottom-up attention model, based on multi-gpu training of Faster R-CNN with ResNet-101, using object and at
StarGAN - Official PyTorch Implementation (CVPR 2018)
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Codes for "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation"
CSDI This is the github repository for the NeurIPS 2021 paper "CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Reinforcement learning algorithms in RLlib
raylab Reinforcement learning algorithms in RLlib and PyTorch. Installation pip install raylab Quickstart Raylab provides agents and environments to b
Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them
TensorFlow Serving + Streamlit! ✨ 🖼️ Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them! This is a pretty simple S
Repository for Project Insight: NLP as a Service
Project Insight NLP as a Service Contents Introduction Features Installation Setup and Documentation Project Details Demonstration Directory Details H
Crowd sourced training data for Rasa NLU models
NLU Training Data Crowd-sourced training data for the development and testing of Rasa NLU models. If you're interested in grabbing some data feel free
An easy-to-use app to visualise attentions of various VQA models.
Ask Me Anything: A tool for visualising Visual Question Answering (AMA) An easy-to-use app to visualise attentions of various VQA models. Please click
An image classification app boilerplate to serve your deep learning models asap!
Image 🖼 Classification App Boilerplate Have you been puzzled by tons of videos, blogs and other resources on the internet and don't know where and ho
This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.
📈 Automated Time Series Forecasting Background: This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to gene
Machine learning library for fast and efficient Gaussian mixture models
This repository contains code which implements the Stochastic Gaussian Mixture Model (S-GMM) for event-based datasets Dependencies CMake Premake4 Blaz
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll
Code, pre-trained models and saliency results for the paper "Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images".
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB This repository is the official implementation of the paper. Our results comming soon in
CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.
CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr
Official Pytorch Implementation for Splicing ViT Features for Semantic Appearance Transfer presenting Splice
Splicing ViT Features for Semantic Appearance Transfer [Project Page] Splice is a method for semantic appearance transfer, as described in Splicing Vi
Using BERT-based models for toxic span detection
SemEval 2021 Task 5: Toxic Spans Detection: Task: Link to SemEval-2021: Task 5 Toxic Span Detection is https://competitions.codalab.org/competitions/2
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll
N-gram models- Unsmoothed, Laplace, Deleted Interpolation
N-gram models- Unsmoothed, Laplace, Deleted Interpolation
TSIT: A Simple and Versatile Framework for Image-to-Image Translation
TSIT: A Simple and Versatile Framework for Image-to-Image Translation This repository provides the official PyTorch implementation for the following p
StarGAN v2 - Official PyTorch Implementation (CVPR 2020)
StarGAN v2 - Official PyTorch Implementation StarGAN v2: Diverse Image Synthesis for Multiple Domains Yunjey Choi*, Youngjung Uh*, Jaejun Yoo*, Jung-W
PyTorch implementation of InstaGAN: Instance-aware Image-to-Image Translation
InstaGAN: Instance-aware Image-to-Image Translation Warning: This repo contains a model which has potential ethical concerns. Remark that the task of
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'
PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi
LightningFSL: Pytorch-Lightning implementations of Few-Shot Learning models.
LightningFSL: Few-Shot Learning with Pytorch-Lightning In this repo, a number of pytorch-lightning implementations of FSL algorithms are provided, inc
A web-based analysis toolkit for the System Usability Scale providing calculation, plotting, interpretation and contextualization utility
System Usability Scale Analysis Toolkit The System Usability Scale (SUS) Analysis Toolkit is a web-based python application that provides a compilatio
CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models
CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models
Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.
Train aug_clip against laion400m-embeddings found here: https://laion.ai/laion-400-open-dataset/ - note that this used the base ViT-B/32 CLIP model. S
TrainingBike - Code, models and schematics I've used to interface my stationary training bike with PC.
TrainingBike Code, models and schematics I've used to interface my stationary training bike with PC. You can find more information about the project i
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models
Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor
A collection of Machine Learning Models To Web Api which are built on open source technologies/frameworks like Django, Flask.
Author Ibrahim Koné From-Machine-Learning-Models-To-WebAPI A collection of Machine Learning Models To Web Api which are built on open source technolog
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.
Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for
BRNet - code for Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function
BRNet code for "Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss func
Codeflare - Scale complex AI/ML pipelines anywhere
Scale complex AI/ML pipelines anywhere CodeFlare is a framework to simplify the integration, scaling and acceleration of complex multi-step analytics
Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan
Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.
Minimal diffusion models - Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)
Minimal code and simple experiments to play with Denoising Diffusion Probabilist
SOTA easy to use PyTorch-based DL training library
Easily train or fine-tune SOTA computer vision models from one training repository. SuperGradients Introduction Welcome to SuperGradients, a free open
Offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation
Shunted Transformer This is the offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation by Sucheng Ren, Daquan Zhou, Shengf
Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019
USSS_ICCV19 Code for Universal Semi Supervised Semantic Segmentation accepted to ICCV 2019. Full Paper available at https://arxiv.org/abs/1811.10323.
Training neural models with structured signals.
Neural Structured Learning in TensorFlow Neural Structured Learning (NSL) is a new learning paradigm to train neural networks by leveraging structured
Ecco is a python library for exploring and explaining Natural Language Processing models using interactive visualizations.
Visualize, analyze, and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
Beibo is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.
Beibo is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.
Contrastive Learning of Structured World Models
Contrastive Learning of Structured World Models This repository contains the official PyTorch implementation of: Contrastive Learning of Structured Wo
An distributed automation framework.
Automation Kit Repository Welcome to the Automation Kit repository! Note: This package is progressing quickly but is not yet ready for full production