An Open Source Machine Learning Framework for Everyone

The new Windows Terminal and the original Windows console host, all in the same place!

LLM inference in C/C++

Open Source Computer Vision Library

A library for efficient similarity search and clustering of dense vectors.

Caffe: a fast open framework for deep learning.

Productive, portable, and performant GPU programming in Python.

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

⭐ Linux / Windows / macOS 跨平台 V2Ray 客户端 | 支持 VMess / VLESS / SSR / Trojan / Trojan-Go / NaiveProxy / HTTP / HTTPS / SOCKS5 | 使用 C++ / Qt 开发 | 可拓展插件式设计 ⭐

Compiler for Neural Network hardware accelerators

Configure Caffe in one hour for Windows users.

Caffe: a fast open framework for deep learning.

A fork of Caffe with OpenMPI-based Multi-GPU (mainly data parallel) support for action recognition and more. More documentation please see the original readme.

Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019

Code and models for "Learning to Compare Image Patches via Convolutional Neural Networks"

A deep learning based algorithm to detect rotated object, for example, objects in remote sensing images

A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (TIP 2018)

Implementation of the paper "No Reference Image Quality Assessment in the Spatial Domain" by A Mittal et al. in OpenCV (using both C++ and Python)

Caffe for CC4.0-Windows，简单的Caffe C++接口，方便简单

A no reference image quality assessment CNN

牛课网练习，主要包括leetcode,剑指offer，Cracking the Coding Interview，PAT，华为上机测试，算法课程学习，一些公司笔试题目等。

Matlab implementation of our TMM 2020 paper "Pixel-level Non-local Image Smoothing with Objective Evaluation"