Machine Vision

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

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.

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

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

A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.

QUick and DIrty Domain Adaptation

Image augmentation library for deep neural networks

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

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.

Light Weight Toolkit for Bounding Boxes

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.

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.

Client library to use the IBM Watson Services

Catalyst. Accelerated deep learning R&D with PyTorch.

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

Image Acquisition Library for GenICam-based Machine Vision System

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

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

Ultralytics HUB Client SDK.

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

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

Computer vision models for PyTorch

Easily turn a set of image urls to an image dataset

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

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

Defining the Future of 3D Machine Vision

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

HuggingFace utilities for Ultralytics/YOLOv8.

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

A rastervision plugin that adds geospatial machine learning pipelines

ML/DL tools function library

Training utility library and config manager for Granular Machine Vision research

Viewing and rendering of sequences of 3D data.

TorchSeg: Semantic Segmentation models for PyTorch

Agnostic Computer Vision Framework

Fast 6DoF Face Alignment and Tracking

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

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

Python Tools for Visual Dataset Transformation

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

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

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.

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.

Find issues in image datasets

Visualize large image collections with WebGL

Training utility library and config manager for Granular Machine Vision research

Project Vi