Machine Vision

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

Fast, flexible, and advanced augmentation library for deep learning, computer vision, and medical imaging. Albumentations offers a wide range of transformations for both 2D (images, masks, bboxes, keypoints) and 3D (volumes, volumetric masks, keypoints) data, 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

Image augmentation library for deep neural networks

QUick and DIrty Domain Adaptation

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.

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 CLI.

Ultralytics HUB Client SDK.

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

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 toolkit for making real world machine learning and data analysis applications

The realtime communication library for Python

Catalyst. Accelerated deep learning R&D with PyTorch.

TorchSeg: Semantic Segmentation models for PyTorch

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

Easily turn a set of image urls to an image dataset

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

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

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

Computer vision models for PyTorch

HuggingFace utilities for Ultralytics/YOLOv8.

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

A Python toolbox for easy interaction with various LLMs and Vision models

A rastervision plugin that adds geospatial machine learning pipelines

computer vision and machine learning library

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

Agnostic Computer Vision Framework

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

🔥LIT: The Learning Interpretability Tool

Find issues in image datasets

Fast 6DoF Face Alignment and Tracking

Defining the Future of 3D Machine Vision

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.

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

ML/DL tools function library

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.

AI Data Infrastructure: Declarative, Multimodal, and Incremental

Training utility library and config manager for Granular Machine Vision research

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

Torchreid-Pip: Deep learning person re-identification in PyTorch.

Data-centric AI building blocks for computer vision applications

Viewing and rendering of sequences of 3D data.