Popular GitHub Repositories for Manual Annotations

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.

A reference containing Styles and Keywords that you can use with MidJourney AI. There are also pages showing resolution comparison, image weights, and much more!

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

AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen? and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency.

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.

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.

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

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

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.

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

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.

Annotations of the interesting ML papers I read

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

A tool for efficient semi-supervised video object segmentation (great results with minimal manual labor) and a dataset for benchmarking