Automate
The most sophisticated labeling automation features in the market.
- Build ensembles of micro-models
- Utilise automation feature suite
- Incorporate problem-specific heuristics
The fastest way to get high quality annotation data for machine learning
Encord's flexible APIs & SDK will allow you to stream annotations directly to your data loaders with a few lines of code.
How it works
Unlock data-centric AI for computer vision
Encord offers the most sophisticated automated data annotation & evaluation solution in the market.
Evaluate
Automated data quality assessments discover errors in your ground truth.
- Version your label sets
- Validate model performance
- Discover classification & geometric errors
Iterate
Seamlessly integrate our APIs & SDK with your model data loaders.
- Import model predictions
- Facilitate active learning pipelines
- Prototype new models and ontologies
Features
Manage, version, experiment
We built Encord to support you as your company adopt and scales computer vision AI applications.
Flexible tools
- Our software support a wide variety of computer vision modalities. Multi-faceted data loaders and parsers fits your use case.
Automated labeling
- Use our object tracking & interpolation features to reduce costs. Use micro-models to accelerate your active learning workflows and get to production faster.
Configurable taxonomy
- Create arbitrarily rich nested labeling structures accommodating all label modalities in one place.
Version
- Iterate and experiment with label structures by branching out projects and adding custom filters.
API & SDK
- Programmatically access, monitoring, and deployment of labels and data assets into your machine learning infrastructure.
Quality
- Access automated assessment and visualisation tools to get precise estimations of your label quality and model performance.
Debug label quality
Use our automated quality control features to ensure only the best ground truth is delivered to your models.
Branch projects
Our labelset management capabilities allow you to experiment fluidly with versioned data and labels, and set up custom pipelines and filters.