The most powerful video annotation tool ever created

State-of-the art object tracking and interpolation features accelerates your video training data creation. Keep track of objects as they enter and leave the frame. Classify actions.

Use cases

Unlock video intelligence

Encord's tools support video labelling for a variety of industries such as government, medical imaging, and smart cities.

Movement & Behaviour

Understand how objects move and interact. Predict behaviour in video footage.

  • Autonomous vehicles
  • Traffic surveillance
  • Human pose estimation

Tracking & Interpolation

Track multiple objects as they enter and leave the frame.

  • Sports analytics
  • Automated retail checkout
  • Battle-space awareness

Action classification

Classify events for specific frame intervals in video.

  • Endoscopy
  • Colonoscopy
  • Telehealth
KCL Logo
King's College London

KCL used Encord to achieve a 6.4x average increase in labelling efficiency for GI videos.

Problem
Using clinicians to annotate pre-cancerous polyp videos had prohibitively high costs to produce large datasets.
Solution
Deployed Encord’s micro-model module to increase clinician labelling efficiency and automate 97% of produced labels.
Results
Highest expense clinician saw 16x labelling efficiency improvement. Cut model development time from 1 year to 2 months.
6.4X
Faster than manual labelling
97%
Automated labels
6X
Faster to AI in production
How it works

Annotate, automate, and evaluate

We built Encord to support you as your data pipelines scale.

Flexible tools

Our software supports a wide variety of video formats (.mp4, .webm., .mkv). Our multi-faceted editor tools facilitate efficient drawing, editing, and reviewing annotations.

Automated labelling

Use our object tracking & interpolation features to reduce costs. Use micro-models to accelerate your active learning workflows and get to production faster.

Configurable ontology

Create arbitrarily rich nested labelling structures accommodating all label modalities in one place.

Quality

Construct customised label review workflows to ensure the highest label quality standards possible.

Collaborative workflows

Our collaboration features allow you to scale your team easily and quickly. Dynamic task queues makes it easy to scale labelling operations.

Visualise

Visualise the breakdown of your labels in high granularity to get precise estimations of your label quality, annotation efficiency, and model performance.
Features 01

Keep track of any object

Our object tracking and interpolation features keep track of unique instance IDs through an entire video, and help to make the most of your human labels.

Features 02

Customise data pipelines

Our APIs & SDK allows for easy building of workflow scripts to converge quickly to successful data strategies. Set up complex pipelines and integrations within minutes.

Technology

Reduce manual annotation

Use automation to save on human supervision and enhance quality.

Encord has developed a wide range of automation features to annotate datasets to the highest quality standards to reduce the bottleneck of manual labour in the annotation process. These features include proprietary sampling, tracking, interpolation, auto-segmentation algorithms, and several intelligent heuristics. However, the core of our technology is a novel approach we call micro-models.

We believed there must be a better way to make AI practical from first starting the company. We have devised a unique and effective methodology for automating and streamlining the tasks related to preparing and managing quality training data.

In contrast to traditional machine learning models that require large quantities of data and are fit for robustness and generalisability, our micro-models are tightly scoped and over-fit to narrow tasks and data distributions.

Our technology allows you to train micro-models in only a few minutes, starting with just a handful of labels, and then ensemble many micro-models together to cover your complete set of labelling tasks. Our platform allows you to assemble micro-models to cover arbitrarily complex annotation tasks.

Get started today