Popular GitHub Repositories for T8n

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

? Assemble, configure, and deploy autonomous AI Agents in your browser.

?Vite+Vue3+Gin拥有AI辅助的基础开发平台，支持TS和JS混用。它集成了JWT鉴权、权限管理、动态路由、显隐可控组件、分页封装、多点登录拦截、资源权限、上传下载、代码生成器、表单生成器和可配置的导入导出等开发必备功能。

stable diffusion webui colab

Your friendliest open source AI automation tool ? Workflow automation tool 200+ integration / Enterprise automation tool / Zapier Alternative

Code for Machine Learning for Algorithmic Trading, 2nd edition.

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.

? Kubectl plugin to create manifests with LLMs

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

Standardized Serverless ML Inference Platform on Kubernetes

Evolution API is an open-source WhatsApp integration API

? A lite C++ toolkit of 100+ Awesome AI models, support ORT, MNN, NCNN, TNN and TensorRT. ??

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo

The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

??? 专注于YOLO11，YOLOv8、TYOLOv12、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules?

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

Images to inference with no labeling (use foundation models to train supervised models).