docs.datatorch.ioIntroduction | DataTorch Documentation

DataTorch Documentation Guide Dev Docs Open-source Open-source Node Storage Geometry.js DataTorch (opens new window) GitHub (opens new window) Guide Dev Docs Open-source Open-source Node Storage Geometry.js DataTorch (opens new window) GitHub (opens new window) Home Introduction Quickstart Sign up Create Project Upload Data Annotate Data Download Annotations Create Export Schema Run Schema Background Project Management Overview Quickstart - Project Set Up Create Project Optional: Upgrade Account Add collaborators Manage Permissions Next Steps Quickstart - Data Set Up Access Filesources Add local files via web UI to default Filesource Add files remotely to a Filesource via Python client Add Google Cloud Bucket as a Filesource Add Azure Blob Storage as a Filesource Add AWS S3 Bucket as a Filesource Create Dataset Assign files to Dataset Projects Storage Providers Datasets Jobs Passthroughs Tasks Organizations Collaborators and permissions Dashboards Filesources Datasets Data Annotation Overview Annotate using Keypoints Create a Keypoint-compatible Label Describe the Keypoint Structure in COCO Format Annotate with the Keypoint Tool Annotate using premade labels Create new labels Annotate with different tools Add metadata to an annotation Use model-assisted annotation tools Create custom model-assisted annotation tools Create schema for exporting annotations Export Schemas Download annotations from schema # Introduction DataTorch is a developer tool for building computer vision models. DataTorch revolves around the management of projects , which encapsulate of all of the data, people, and work related to a particular model. # Quickstart Get started annotating a dataset and exporting it in COCO format right away. Get Started → # Project Management Organize datasources, datasets, ontologies, and people with DataTorch. Learn More → # Data Annotation Create a variety of annotation types, collaboratively annotate data, and export result in different formats. Learn More → # Automation Utilize Agents and Pipelines to automate processes in your machine learning development process. Learn More → Edit this page on GitHub (opens new window) Last Updated: a year ago Quickstart...