Machine Vision

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Fast, flexible, and advanced image augmentation library for deep learning and computer vision. Albumentations offers a wide range of transformations for images, masks, bounding boxes, and keypoints, with optimized performance and seamless integration into ML workflows.

John Snow Labs Spark NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment.

A set of easy-to-use utils that will come in handy in any Computer Vision project

QUick and DIrty Domain Adaptation

Image augmentation library for deep neural networks

A toolkit for making real world machine learning and data analysis applications

Light Weight Toolkit for Bounding Boxes

Packaged version of the Yolov5 object detector

With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference.

Catalyst. Accelerated deep learning R&D with PyTorch.

With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference.

Image Acquisition Library for GenICam-based Machine Vision System

A research and production integrated edge-cloud library for federated/distributed machine learning at anywhere at any scale.

Ultralytics HUB Client SDK.

With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference CLI.

With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference.

A toolkit for making real world machine learning and data analysis applications

Easily turn a set of image urls to an image dataset

This is the module for detecting and classifying text on rama pictures

An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application

A package designed to predict static pose and detect falls with 2D RGB Camera in well lit indoor environments.

TorchSeg: Semantic Segmentation models for PyTorch

Pymba is a Python wrapper for Allied Vision's Vimba C API.

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

Computer vision models for PyTorch

HuggingFace utilities for Ultralytics/YOLOv8.

A lightweight Computer Vision library for high-performance AI research - Modern Computer Vision on the Fly.

Easily computing clip embeddings and building a clip retrieval system with them

A computer vision pipeline for the semantic exploration of maps/images at scale

With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference.

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint

A rastervision plugin that adds geospatial machine learning pipelines

Agnostic Computer Vision Framework

With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using Roboflow Inference.

ML/DL tools function library

🔥LIT: The Learning Interpretability Tool

Defining the Future of 3D Machine Vision

Fast 6DoF Face Alignment and Tracking

Training utility library and config manager for Granular Machine Vision research

Find issues in image datasets

An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.

:warning: The `yolov8` package is a placeholder, not the official Ultralytics version. Please install the official `ultralytics` package via `pip install ultralytics` instead.

Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

AI Data Infrastructure: Declarative, Multimodal, and Incremental