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

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

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.

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.

Image Acquisition Library for GenICam-based Machine Vision System

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

Ultralytics HUB Client SDK.

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.

Computer vision models for PyTorch

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

A rastervision plugin that adds geospatial machine learning pipelines

Easily turn a set of image urls to an image dataset

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

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

HuggingFace utilities for Ultralytics/YOLOv8.

TorchSeg: Semantic Segmentation models for PyTorch

Viewing and rendering of sequences of 3D data.

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

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

Find issues in image datasets

Automation of the creation of the architecture of the neural network based on the input

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

🔥LIT: The Learning Interpretability Tool

Training utility library and config manager for Granular Machine Vision research

Agnostic Computer Vision Framework

Packaged version of the ByteTrack repository

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.

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.

doc2text drastically improves the extraction of text from images by fixing resolution, text area (crop), and skew.

: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

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.

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

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

Python package for the EXE3002 - Classifiers and Machine Vision written assignment