Popular GitHub Repositories for Hypothesis 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.

?? 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.

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.

DATAGEN: AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing. Now expanding into crypto market intelligence. Learn more: https://datagen.digital/.

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.

Enabling easy statistical significance testing for deep neural networks.

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

PyTorch Model Compression

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.