Collaborative Computer Vision Tools for Teams

Our software is designed to handle the varying requirements and modalities of visual data.

Use cases

Consolidated computer vision workflows

Our suite of powerful tools allows for seamless collaboration across roles and teams, from domain-expert annotators to project managers and machine learning engineers. Learn more about our image, video, and DICOMtools.

Smart cities

Local governments & cities are reducing traffic and pollution with analytics powered by computer vision.

  • Curbside monitoring
  • Traffic management
  • Public safety surveillance

Sports analytics

Sport teams are gaining a competitive edge in tournaments by discovering insights with AI.

  • Badminton pose adjustment
  • Football team formation analytics
  • Personalised coaching

Retail

Retailers are using AI to locate items, gather intelligence on purchasing behaviour, and much more.

  • Automated checkouts
  • Item detection
  • Stock monitoring

Agriculture

Farmers use robotic crop harvesting and seasonal yield diagnostics to improve efficiency and reduce wastage.

  • Self-driving tractors
  • Autonomous harvesters
  • Livestock surveillance

Autonomous transportation

Autonomous vehicles are poised to revolutionise the way societies are structured.

  • Long-haul autonomous trucking
  • Driverless taxi services
  • Self-flying planes

AR/VR

The meta-verse is here and is already transforming our social lives with gaming.

  • Human pose estimation
  • MMORPG games
  • Blending digital and analogue worlds
KCL Logo
Leading restaurant automation provider

The automation provider tracked objects across different views & replaced 37 annotation hours with 1.

Problem
Tracking objects across different views coupled with changing weather & lighting conditions led to annotation inconsistencies & high costs.
Solution
Deployed Encord's micro-model & interpolation modules to track objects across different views, enforce consistency, & increase labeling efficiency.
Results
37x increase in labeling efficiency. Annotation accuracy increased from 94% to 99%.
37X
Faster than manual labeling
4pp
Accuracy boost
99%
Annotation accuracy
How it works

Annotation, training, deployment

Power models from segmentation to pose detection using training data generated through Encord.

Flexible tools

Our software supports a wide variety of computer vision modalities. Classify & annotate bounding boxes, polygons, key points, and segments in a single editor.

Model-assisted labeling

Use our native micro-model technology to reduce the manual annotation burden and pivot human supervision from labeling to quality control.

Configurable label editor

Set up your own label structures with infinitely nested attributes and hierarchical relationships. Apply nested classifications and preserve conditional relationships between features.

Quality

Create custom annotation & review pipelines with our intuitive interface. Discover poorly performing annotators using our performance dashboards, benchmark & consensus features.

Collaboration made easy

Role-based access control, annotator performance tracking, and dynamic task queues make massive-scale labeling operations a bliss.

Visualise

Reduce time to production by spotting data biases and imbalances early. Discover & visualise errors in your datasets.
Automate with micro-models

Automate with micro-models

Augment your human workforce with our novel micro-model technology and flexible labeling tools.

Understand label quality

Understand label quality

Use our automated quality control features to ensure only the best ground truth is delivered to your models.

Undraw surveillance
Top-3 human behaviour surveillance AI company

The company built custom label structures with Encord's adaptive ontology & automated workflows with Encord's SDK.

Problem
Complex annotation tasks rendered existing & open-source tools unusable. GDPR & privacy restrictions prohibited use of managed service.
Solution
Utilised Encord to build custom pose estimation templates, integrate to their cloud for compliance, operate tracking modules to automate annotations.
Results
Built a highly sophisticated human behaviour dataset. Automation features made in-house workforce practical.
GDPR & Privacy
Compliant
Custom label structures
Flexible ontology
97%
Automated labels

Train computer vision models faster with Encord