
Classification
Apply nested and higher order classes to an entire image.
- Self-driving cars
- Traffic surveillance
- Visual content moderation
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.
Encord's tools support image annotation for a variety of industries such as healthcare, government, and computer vision.
Apply nested and higher order classes to an entire image.
Recognise and localise objects with vector labelling tools.
Assign a class to each pixel of an image with segmentation masks.
The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.
We built Encord to support you as your company adopt and scales computer vision AI applications.
Flexible tools
Model-assisted labelling
Configurable label editor
Quality
Collaboration made easy
Visualise
Our flexible and intuitive image labelling tools will make manual annotations and reviews a breeze.
Track annotator throughput and quality with our labelling dashboard to make the most of your annotation team.
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.