Conquer the daily challenges of annotating and validating your images
When it comes to maximizing the performance of your machine learning models, having good quality annotations is one of the best places to start. Getting it right from the very beginning will set up your project for success and help to prevent potential ripple down effects occurring at the later stages.
Despite the clear benefits, obtaining good annotations is a time consuming, nuanced and expensive exercise.
Zegami Image Annotator has been designed from the ground up to solve many of the problems encountered when annotating large image data sets. It’s a visual, intuitive and fast way to allocate annotation tasks, create bounding box, polygon or segmentation masks, compare annotator performance against a gold standard and then integrate them into your existing machine learning workflow.
Image Annotator is fully integrated with the Machine Learning Suite. All annotations are stored alongside the images for effortless collaboration and reporting. Subset your data into classes, automatically cluster similar looking images and identify bias. Having this overview of your data saves days of wasted effort when image quality issues are discovered part-way through an annotation project.
Quickly tag and separate your data into subsets, based on a predefined query or specific visual characteristics. Tag images for testing and validation, assign images to specific annotators. Use Zegami’s powerful exploration and filtering features such as ‘image similarity view’ to quickly mark those that need attention.
Create pixel perfect segmentations at pace! Our smart segmentation tool takes the work out of painfully painting ROI’s. Just mark the area and provide a hint. Job done.
Most annotation tools are tedious to work with. Drawing boxes around thousands of objects makes it difficult to maintain focus, impacting the quality of your models. With Image Annotator creating masks is straightforward: a simple box, a quick adjustment and you’re done. If you prefer drawing polygons or painting masks by hand, then you are free to do so.
Collaborate with Image Annotator to share the workload across the team, peer review each other’s work and track overall progress of the project. Whether tracking annotations by the person that made them or by class, Zegami makes visualising your assets simple.
Compare annotator performance
Coordinating the efforts between multiple annotators can be difficult and identifying where quality is slipping is even more so. Easily compare annotations against the gold standard, other annotators or even a trained model for full insight.
With approvals, you can monitor and review annotations in real time and catch issues before they make their way into training.
How it works
Once the target images have been identified, you can then view and annotate your target images in Zegami Image Annotator. The tool makes it easy to draw bounding boxes, polygons and pixel-perfect segmentations with both manual and algorithm-driven tools. All annotations are automatically synchronised to Zegami so they can be accessed anywhere and by any of your collaborators. The resulting annotations can be exported in a convenient, customisable format that can then be integrated into your machine learning workflow.