Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 β object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
We write your reusable computer vision tools. π
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
π« Industrial-strength Natural Language Processing (NLP) in Python
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.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
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.
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
An open source implementation of CLIP.
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.
A unified framework for machine learning with time series
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
π :bar_chart: :bulb: Orange: Interactive data analysis
MTEB: State-of-the-art evaluation of embeddings across languages and modalities
The machine learning toolkit for time series analysis in Python
Images to inference with no labeling (use foundation models to train supervised models).
Turn any computer or edge device into a command center for your computer vision projects.
The collection of pre-trained, state-of-the-art AI models for ailia SDK
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
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
A Python package for time series classification
Python AutoML for Trading Systems and Sports Betting
A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. π26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. π Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
Multi-class confusion matrix library in Python
Fast and customizable framework for automatic ML model creation (AutoML)
A toolkit for time series machine learning and deep learning
π¦ Integrating LLMs into structured NLP pipelines
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).
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.
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using β‘ Pytorch Lightning and π€ Transformers.
Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
Neural Network Compression Framework for enhanced OpenVINOβ’ inference
Local-first privacy guard: anonymize your documents before sharing with LLMs.
Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python
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.
High-performance Vision library in Python. Scale your research, not boilerplate.
[CVPR2026]πππOfficial code for the paper "YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection." *(YOLO = You Only Look Once)* π₯π₯π₯
NucliaDB, The AI Search database for RAG
A cautious, explainable AI text detector for local workflows and coding agents. Need production detection in your app? Try the Lynote AI Detector API β lynote.ai/ai-detector-api
Clusters and elements to attach to MISP events or attributes (like threat actors)
Active Learning for Text Classification in Python
Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application
GTDB-Tk: a toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes.
Span-level grounding verification for RAG, code, and tool-grounded AI outputs.
1207 public resources for Jev, TypeSafe AI's System One decision model, indexed by decision pattern. Source citations, dated link checks and scheduled call-site text checks; runtime and performance are not independently tested here. EN/δΈζ, JSON schema and platform compatibility.
A flexible, adaptive classification system for dynamic text classification
Image augmentation for computer vision. AGPL-3.0-only or commercial licensing.
Generalist and Lightweight Model for Text Classification
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.
Quickly search, compare, and analyze genomic and metagenomic data sets.
π₯π₯ Awesome Jev: open-source models, projects, benchmarks, and independent evaluations for Jev and System One models.
Build calibrated AI Functions from human feedback using Jev and GEPA.
A self-hosted drop-in replacement for TypeSafe's jev, powered by GliFormer.
Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)
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%, 2.1Γ faster ingest, 68% fewer tokens.