Popular GitHub Repositories for Annotations Processor

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

?? List of satellite image training datasets with annotations for computer vision and deep learning

The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

A list of ICs and IPs for AI, Machine Learning and Deep Learning.

A collection of corpora for named entity recognition (NER) and entity recognition tasks. These annotated datasets cover a variety of languages, domains and entity types.

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

YEDDA: A Lightweight Collaborative Text Span Annotation Tool. Code for ACL 2018 Best Demo Paper Nomination.

Sidequest is a modern, scalable background job processor for Node.js applications.

Anno-Mage: A Semi Automatic Image Annotation Tool which helps you in annotating images by suggesting you annotations for 80 object classes using a pre-trained model

Label Objects and Save Time (LOST) - Design your own smart Image Annotation process in a web-based environment.

Want a faster ML processor? Do it yourself! -- A framework for playing with custom opcodes to accelerate TensorFlow Lite for Microcontrollers (TFLM). . . . . . Online tutorial: https://google.github.io/CFU-Playground/ For reference docs, see the link below.

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

SpatialVID: A Large-Scale Video Dataset with Spatial Annotations

Deep Learning Autonomous Car based on Raspberry Pi, SunFounder PiCar-V Kit, TensorFlow, and Google's EdgeTPU Co-Processor

[ICLR 2025] This is the official repository of our paper "MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine“

The largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations), up until April 2023.

Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.

Code used to generate synthetic scenes and bounding box annotations for object detection. This was used to generate data used in the Cut, Paste and Learn paper

ML data annotations made super easy for teams. Just upload data, add your team and build training/evaluation dataset in hours.