Current rates of data growth means that we will rapidly run out people to review data.
Automatically Find & Resolve Errors in Image and Video Annotation Data
Our label quality evaluation tool helps you to automatically find classification and geometric errors in your training data, ensuring that your labels are of the highest possible quality before they go into production.
The problem with existing methods of annotation quality assessment
Human supervision can be highly unreliable, biased, and redundant. It also adds severe constraints on throughput where high expertise is necessary.
Human perspectives differ, which can create disagreements on what is ground truth.
Humans may be reviewing data that might not be value-add to your model.
Augment quality assessment with automation
Encord's novel quality assessment tool helps scale your quality control processes by spotting hidden errors in your training dataset.
- Let algorithms do the grunt work - deploy humans only when necessary
- Easy to use interface allows you to assess multiple label types.
- Finally a tool that helps you discover and visualise errant labels.
A new approach to annotation and segmentation accuracy
Encord has developed the first truly automated quality assessment tool - powered by micro-models. Instantly discover errors in your labels and make the most of human review.
Import model predictions through our APIs and SDK to find errors and biases in your model.
Predict label quality
Use our automated quality control features to ensure only the best ground truth is delivered to your models.
Encord supports purpose-built workflows for domain experts to drive specialised quality control.