Popular GitHub Repositories for Fairness Comparison

A reference containing Styles and Keywords that you can use with MidJourney AI. There are also pages showing resolution comparison, image weights, and much more!

? Open-Source Evaluation & Testing for AI & LLM systems

Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.

Facilitating the design, comparison and sharing of deep text matching models.

A curated list of awesome responsible machine learning resources.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

A Python package to assess and improve fairness of machine learning models.

Analysis, Comparison, Trends, Rankings of Open Source Software, you can also get insight from more than 7 billion with natural language (powered by OpenAI). Follow us on Twitter: https://twitter.com/ossinsight

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.

Integrate deep learning models for image classification | Backbone learning/comparison/magic modification project

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

ARCH models in Python

Human ChatGPT Comparison Corpus (HC3), Detectors, and more! ?

LeGO-LOAM, LIO-SAM, LVI-SAM, FAST-LIO2, Faster-LIO, VoxelMap, R3LIVE, Point-LIO, KISS-ICP, DLO, DLIO, Ada-LIO, PV-LIO, SLAMesh, ImMesh, FAST-LIO-MULTI, M-LOAM, LOCUS, SLICT, MA-LIO, CT-ICP, GenZ-ICP, iG-LIO, SR-LIO application and comparison on Gazebo and real-world datasets. Installation and config files are provided.

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

? The experiment tracker for foundation model training

An experimental platform for federated learning.

Discover top AI coding tools for developers, including code editors, completion engines, and natural language converters. Enhance your workflow with AI! ?️?