Python Tensorflow-federated-learning Resources

Telemanom (v2.0) v2.0 updates: Vectorized operations via numpy Object-oriented restructure, improved organization Merge branches into single branch fo

pymc-learn: Practical Probabilistic Machine Learning in Python Contents: Github repo What is pymc-learn? Quick Install Quick Start Index What is pymc-

DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

LOFO (Leave One Feature Out) Importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, with a validation scheme of choice, based on the chosen metric.

partial_dependence A python library for plotting partial dependence patterns of machine learning classifiers. The technique is a black box approach to

XAI - An eXplainability toolbox for machine learning XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contai

moDel Agnostic Language for Exploration and eXplanation Overview Unverified black box model is the path to the failure. Opaqueness leads to distrust.

InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet buil

NNI Doc | 简体中文 NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture

automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn

Neural Architecture Search Powered by Swarm Intelligence 🐜 DeepSwarm DeepSwarm is an open-source library which uses Ant Colony Optimization to tackle

Optuna: A hyperparameter optimization framework Website | Docs | Install Guide | Tutorial Optuna is an automatic hyperparameter optimization software

River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on streaming data.

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o

Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play

Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions.

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

m2cgen (Model 2 Code Generator) - is a lightweight library which provides an easy way to transpile trained statistical models into a native code

sklearn-porter Transpile trained scikit-learn estimators to C, Java, JavaScript and others. It's recommended for limited embedded systems and critical

ModelChimp What is ModelChimp? ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments. ModelChimp provides the followi

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

Knock Knock A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of co

Metaflow Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow

Machine Learning Videos This is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

GSCNN This is the official code for: Gated-SCNN: Gated Shape CNNs for Semantic Segmentation Towaki Takikawa, David Acuna, Varun Jampani, Sanja Fidler

PyTorch-Encoding created by Hang Zhang Documentation Please visit the Docs for detail instructions of installation and usage. Please visit the link to

Medical Image Segmentation with Guided Attention This repository contains the code of our paper: "'Multi-scale self-guided attention for medical image

UPSNet: A Unified Panoptic Segmentation Network Introduction UPSNet is initially described in a CVPR 2019 oral paper. Disclaimer This repository is te

TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under

Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s

Pixel Transposed Convolutional Networks Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University. Introduction Pixel

KittiSeg KittiSeg performs segmentation of roads by utilizing an FCN based model. The model achieved first place on the Kitti Road Detection Benchmark

MultiNet MultiNet is able to jointly perform road segmentation, car detection and street classification. The model achieves real-time speed and state-

Tensorflow implementation of Learning Deconvolution Network for Semantic Segmentation. Install Instructions Works with tensorflow 1.11.0 and uses the

DeconvNet: Learning Deconvolution Network for Semantic Segmentation Created by Hyeonwoo Noh, Seunghoon Hong and Bohyung Han at POSTECH Acknowledgement

Training Reproduce of PSPNet. (Updated 2021/04/09. Authors of PSPNet have provided a Pytorch implementation for PSPNet and their new work with support

PSPNet_tensorflow Important Code is fine for inference. However, the training code is just for reference and might be only used for fine-tuning. If yo

Keras implementation of PSPNet(caffe) Implemented Architecture of Pyramid Scene Parsing Network in Keras. For the best compability please use Python3.

PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+

Real-Time Semantic Segmentation in TensorFlow Perform pixel-wise semantic segmentation on high-resolution images in real-time with Image Cascade Netwo

ICNet_tensorflow This repo provides a TensorFlow-based implementation of paper "ICNet for Real-Time Semantic Segmentation on High-Resolution Images,"

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Keras implementation of DilatedNet for semantic segmentation A native Keras implementation of semantic segmentation according to Multi-Scale Context A

FC-DenseNet-Tensorflow This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). Th

TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

semantic-segmentation-tensorflow This is a Tensorflow implementation of semantic segmentation models on MIT ADE20K scene parsing dataset and Cityscape

FCN-semantic-segmentation Simple end-to-end semantic segmentation using fully convolutional networks [1]. Takes a pretrained 34-layer ResNet [2], remo

Pytorch for Semantic Segmentation This repo has been deprecated currently and I will not maintain it. Meanwhile, I strongly recommend you can refer to

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation. #USAGE To run the trained classifier on some images: python w

FCN.tensorflow Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (FCNs). The implementation is largely based on the

Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

TorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{you2019torchcv, author = {Ansheng You and Xiangtai Li and Zhen Zhu a

TF-deeplab This is a Tensorflow implementation of DeepLab, compatible with Tensorflow 1.2.1. Currently it supports both training and testing the ResNe

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Up

DeepLab-ResNet-TensorFlow This is an (re-)implementation of DeepLab-ResNet in TensorFlow for semantic image segmentation on the PASCAL VOC dataset. Fr

DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute

SegNet SegNet is a model of semantic segmentation based on Fully Comvolutional Network. This repository contains the implementation of learning and te

Tensorflow-SegNet Implement slightly different (see below for detail) SegNet in tensorflow, successfully trained segnet-basic in CamVid dataset. Due t

SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a

Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm

Python library with Neural Networks for Image Segmentation based on Keras and TensorFlow. The main features of this library are: High level API (just

Real-Time Semantic Segmentation in Mobile device This project is an example project of semantic segmentation for mobile real-time app. The architectur

Tensorflow Unet Warning This project is discontinued in favour of a Tensorflow 2 compatible reimplementation of this project found under https://githu

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

A combination of autoregressors and autoencoders using XLNet for sentiment analysis Abstract In this paper sentiment analysis has been performed in or

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

shredder-Machine-Hand-Safety The main aim of this project is to avoid the accidents in shredding ( Waste Recycling Industry ) . The Basic function of

Identifies the faulty wafer before it can be used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The project retrains itself after every prediction, making it more robust and generalized over time.

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat

AlbUNet-1D-2D-Tensorflow-Keras This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed

TensorLight is a high-level framework for TensorFlow-based machine intelligence applications. It reduces boilerplate code and enables advanced feature

FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of protein targets. The dataset is presented with a model evaluation benchmark which aims to drive few-shot learning research in the domain of molecules and graph-structured data.

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

The-Emergence-of-Objectness This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

TTSR Official PyTorch implementation of the paper Learning Texture Transformer Network for Image Super-Resolution accepted in CVPR 2020. Contents Intr

SusPedal-Gen This is the official repository of Learning to Generate Piano Music With Sustain Pedals Demo Page Dataset The dataset used in this projec

Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis

Doom o’clock is a website/project that features a countdown of “when will the earth end” and a greenhouse gas effect emission prediction that’s predicted

Luga A blazing fast language detection using fastText's language models Luga is a Swahili word for language. fastText provides a blazing fast language

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end

Reproducibility materials for "Importance of Kernel Bandwidth in Quantum Machine Learning" Repo structure: code contains Python scripts used to genera

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

isvd Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning If you find this code useful, you may cite us as: @inprocee

Ankiniser A Python3.8 script to convert your favorite TV series into an Anki deck. How to install? Download the script with git or download it manualy

Sparrow is our open-source ML product. It runs on Skipper MLOps infrastructure.

Dense Unsupervised Learning for Video Segmentation This repository contains the official implementation of our paper: Dense Unsupervised Learning for

Learning Signal-Agnostic Manifolds of Neural Fields This is the uncleaned code for the paper Learning Signal-Agnostic Manifolds of Neural Fields. The

Deep Neural Network Prediction Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num