State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
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.
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.
An efficient library for image augmentation, providing extensive transformations to support machine learning and computer vision tasks.
QUick and DIrty Domain Adaptation
A set of easy-to-use utils that will come in handy in any Computer Vision project
A toolkit for making real world machine learning and data analysis applications
Image augmentation library for deep neural networks
Light Weight Toolkit for Bounding Boxes
Packaged version of the Yolov5 object detector
Client library to use the IBM Watson Services
Ultralytics HUB Client SDK.
Easily turn a set of image urls to an image dataset
A toolkit for making real world machine learning and data analysis applications
Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such as vision and text.
Image Acquisition Library for GenICam-based Machine Vision System
Find issues in image datasets
A research and production integrated edge-cloud library for federated/distributed machine learning at anywhere at any scale.
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.
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.
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.
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
HuggingFace utilities for Ultralytics/YOLOv8.
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.
A lightweight Computer Vision library for high-performance AI research - Modern Computer Vision on the Fly.
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
Defining the Future of 3D Machine Vision
Easily computing clip embeddings and building a clip retrieval system with them
🔥LIT: The Learning Interpretability Tool
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.
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Agnostic Computer Vision Framework
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
Training utility library and config manager for Granular Machine Vision research
Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint
Pymba is a Python wrapper for Allied Vision's Vimba C API.
Ultralytics YOLOv8 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.
doc2text drastically improves the extraction of text from images by fixing resolution, text area (crop), and skew.
Viewing and rendering of sequences of 3D data.
Python Tools for Visual Dataset Transformation
Visualize large image collections with WebGL
An inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
A rastervision plugin that adds geospatial machine learning pipelines
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Easiest way of fine-tuning HuggingFace video classification models.