Popular GitHub Repositories for Uml State Diagram

? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

The Memory layer for AI Agents

? Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

State-of-the-art 2D and 3D Face Analysis Project

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

State-of-the-art Machine Learning for the web. Run ? Transformers directly in your browser, with no need for a server!

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

A collection of pre-trained, state-of-the-art models in the ONNX format

[NeurIPS 2024 Best Paper][GPT beats diffusion?] [scaling laws in visual generation?] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!

This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like YOLO11, RT-DETR, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5VL.

a state-of-the-art-level open visual language model | 多模态预训练模型

AI suite powered by state-of-the-art models and providing advanced AI/AGI functions. It features AI personas, AGI functions, multi-model chats, text-to-image, voice, response streaming, code highlighting and execution, PDF import, presets for developers, much more. Deploy on-prem or in the cloud.

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

State of the Art Natural Language Processing

:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)

Official implementation for the paper: "Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering""