Stars
Accepted as [NeurIPS 2024] Spotlight Presentation Paper
🦜🔗 The platform for reliable agents.
vits2 backbone with multilingual-bert
🎙️🤖Create, Customize and Talk to your AI Character/Companion in Realtime (All in One Codebase!). Have a natural seamless conversation with AI everywhere (mobile, web and terminal) using LLM OpenAI …
Code and documentation to train Stanford's Alpaca models, and generate the data.
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.
Jiagu深度学习自然语言处理工具 知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。
Real-Time High-Resolution Background Matting
High-Resolution 3D Human Digitization from A Single Image.
🏡 Open source home automation that puts local control and privacy first.
A faster pytorch implementation of faster r-cnn
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Image augmentation library in Python for machine learning.
A lightweight, simple-to-use, RNN wake word listener
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
INFO-SPIDER 是一个集众多数据源于一身的爬虫工具箱🧰，旨在安全快捷的帮助用户拿回自己的数据，工具代码开源，流程透明。支持数据源包括GitHub、QQ邮箱、网易邮箱、阿里邮箱、新浪邮箱、Hotmail邮箱、Outlook邮箱、京东、淘宝、支付宝、中国移动、中国联通、中国电信、知乎、哔哩哔哩、网易云音乐、QQ好友、QQ群、生成朋友圈相册、浏览器浏览历史、12306、博客园、CSDN博客…
手写实现李航《统计学习方法》书中全部算法
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
AlexeyAB / darknet
Forked from pjreddie/darknetYOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
Detects the sitting position of a person
Keras implementation of RetinaNet object detection.
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
A PyTorch Implementation of Single Shot MultiBox Detector
OpenMMLab Detection Toolbox and Benchmark
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.