Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
斑海豹 — Phocinae-Largha-150M-v1: a 144.3M bilingual decision model. Tiny model. Big decisions. 小海豹,大决断。
Label Studio is a multi-type data labeling and annotation tool with standardized output format
We write your reusable computer vision tools. 💜
Small decision models (jev style) for latency critical scenarios
Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.
X-AnyLabeling: A lightweight, efficient, and unified cross-platform desktop application for annotating text, image, video, point cloud and multimodal data, combining versatile built-in tools with state-of-the-art AI models and flexible multi-format export.
Self-hosted decision model (selfjev-4b) with a Jev-compatible API: Python SDK, server, fine-tuning and RLCD.
Account screening, channel screening and classification with Jev for dataset curation and research. Find sign language, news, piano/instrument performance and makeup tutorial creators from titles and thumbnails. 视频平台工具:批量筛选账号与频道,寻找手语账号、新闻频道、乐器演奏与美妆博主。Douyin, Kuaishou, YouTube, Bilibili.
Run open decision models locally: pull and serve Laya, decider, NLI and GLiClass behind a TypeSafe-compatible API. Ollama for decision models.
Run Laya, the open-source Jev-compatible System-1 decision model, from Node.js / TypeScript via ONNX Runtime
CVAT is a leading data annotation platform for image, video, audio, and 3D datasets. It offers open-source, cloud, and enterprise products, as well as labeling services, with AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Refine high-quality datasets and visual AI models
💫 Industrial-strength Natural Language Processing (NLP) in Python
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Awesome list of TypeSafe AI Jev use cases: 74 demos ranked by likes, 150+ GitHub repos, limits, cost and API examples. CC0
Fast and accurate AI powered file content types detection
Local-first privacy guard: anonymize your documents before sharing with LLMs.
DSH Web UI plugin: auto-sends 「继续」 after non-human interruptions, with error classification, adaptive backoff, pause, idempotency/loop guards, resume templates, and stats—all configurable in settings UI | DSH 前端 UI 插件:非人为中断后自动发送「继续」,支持错误分类、自适应退避、暂停、幂等/循环保护、续跑模板与统计,均可在设置界面配置
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
An Awesome List of the latest time series papers and code from top AI venues.
A curated list of Jev use cases, projects, SDKs, and resources. Jev is TypeSafe AI's System One model for fast, typed decisions in software — Choice, Score, and Noul with calibrated probabilities.
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
🍊 :bar_chart: :bulb: Orange: Interactive data analysis
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
A unified framework for machine learning with time series
Build, train, optimize, and run computer vision models locally, from raw images to live inference. Open source, optimized for Intel XPU (CPU-only and CUDA also supported).
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers.
Image augmentation for computer vision. AGPL-3.0-only or commercial licensing.
Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
An open source implementation of CLIP.
Techniques for deep learning with satellite & aerial imagery
A 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 RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
An index of algorithms for learning causality with data
The machine learning toolkit for time series analysis in Python
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
A Julia machine learning framework
A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
A toolkit for time series machine learning and deep learning
A Docker-powered service for PDF document layout analysis. This service provides a powerful and flexible PDF analysis service. The service allows for the segmentation and classification of different parts of PDF pages, identifying the elements such as texts, titles, pictures, tables and so on.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python
A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.
High-performance Vision library in Python. Scale your research, not boilerplate.
[CVPR2026]🚀🚀🚀Official code for the paper "YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection." *(YOLO = You Only Look Once)* 🔥🔥🔥
Categorise transactions in Actual Budget using AI.
A self-hosted drop-in replacement for TypeSafe's jev, powered by GliFormer.
Turn your documents into a Markdown wiki you can read, that answers with citations. Works with any LLM provider, Ollama included. CROW classifier routing: 89% right note vs 62%, 2.1× faster ingest, 68% fewer tokens.
Software, by reflex. An open runtime that turns human intent into inspectable plans across small, composable programs, with explicit permissions and human approval before irreversible actions.
Statistical Machine Intelligence & Learning Engine
All-in-One Development Tool based on PaddlePaddle
Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
A treasure chest for visual classification and recognition powered by PaddlePaddle