Image labelling, reimagined

Our fast and intuitive collaborative annotation tools enrich your data so that you can build cutting-edge AI applications. Encord automatically classifies, detects, segments, and tracks objects in images.

Use cases

Build computer vision models faster

Encord's tools support image annotation for a variety of industries such as healthcare, government, and computer vision.

Classification

Apply nested and higher order classes to an entire image.

  • Self-driving cars
  • Traffic surveillance
  • Visual content moderation

Object detection

Recognise and localise objects with vector labelling tools.

  • Gastroenterology
  • Automated retail checkout
  • Drone surveillance

Segmentation

Assign a class to each pixel of an image with segmentation masks.

  • Stroke segmentation
  • Pathology in microscopy
  • Virtual fitting rooms
Stanford Medicine
Stanford Medicine

The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.

Problem
Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Solution
Stanford started using Encord’s annotation tools & SDK to automate segmentations, count, and calculate sizes of segments.
Results
With Encord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
80%
Reduction in experiment duration
3X
Number of images
1 platform
... and not 3
How it works

Collaborate, automate, and evaluate

We built Encord to support you as your company adopt and scales computer vision AI applications.

Flexible tools

Our software supports a wide variety of image formats (.png, .jpg., .tiff). Classify & annotate bounding boxes, polygons, key points, and segments in a single editor.

Model-assisted labelling

Use our native micro-model technology to reduce the manual annotation burden and pivot human supervision from labelling 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 labelling operations a bliss.

Visualise

Reduce time to production by spotting data biases and imbalances early. Discover & visualise errors in your datasets.
Features 01

Give your annotators superpowers

Our flexible and intuitive image labelling tools will make manual annotations and reviews a breeze.

Features 02

Keep tabs on performance

Track annotator throughput and quality with our labelling dashboard to make the most of your annotation team.

Technology

Minimising human labelling

Use automation to save on annotation costs, improve quality, and get to production AI faster

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