Popular GitHub Repositories for Cloudfront Distribution

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement [ICLR 2025 Spotlight]

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

The basic distribution probability Tutorial for Deep Learning Researchers

SHARK Studio -- Web UI for SHARK+IREE High Performance Machine Learning Distribution

?云原生、超高性能 AI&API网关，LLM API 管理、分发系统、开放平台，支持所有AI API，不限于OpenAI、Azure、Anthropic Claude、Google Gemini、DeepSeek、字节豆包、ChatGLM、文心一言、讯飞星火、通义千问、360 智脑、腾讯混元等主流模型，统一 API 请求和返回，API申请与审批，调用统计、负载均衡、多模型灾备。一键部署，开箱即用。Cloud native, ultra-high performance AI&API gateway, LLM API management, distribution system, open platform, supporting all AI APIs.

A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition

Benchmarking Generalized Out-of-Distribution Detection

Lightweight, useful implementation of conformal prediction on real data.

Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

全新的高颜值/高性能的AI模型接口管理与分发系统，仅供个人学习使用，请勿用于任何商业用途，本项目基于NewAPI开发。A brand new high aesthetic/high-performance AI model interface management and distribution system is only for personal or internal management and distribution channels of enterprises. Please do not use it for commercial purposes.

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection, NeurIPS2020

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Python package for conformal prediction

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021

Official PyTorch implementation of Fully Attentional Networks

This is an unofficial implementation of the paper “PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization”.

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.