Zegami combines advanced analysis tools with a unique visualisation interface and allows you to rapidly categorise, label and clean large image datasets, invaluable for many applications including training machine learning models.
What is Zegami?
Easily explore large image datasets and create powerful insight by combining advanced analysis tools with a unique visualisation interface. Using Zegami you will be able to rapidly categorise, label and clean large image datasets, invaluable for many applications including training machine learning models. Zegami was developed so that it could be used to visualise other kinds of data in addition to images, including video, documents or more traditional data sources like databases and spreadsheets.
Zegami is cloud-native, making use of Azure Kubernetes Service to process images and data, as well as using Azure Storage Service.
How do organisations use Zegami?
Zegami is unique because it easily works with both structured & unstructured data, from multiple sources: from images to documents and APIs to video. Alternatives to this are extremely time-consuming, involving multiple stages, data cleaning and several software packages.
Based on Oxford University research and inspired by Microsoft PivotViewer software, Zegami is being used by academics and commercial leaders across the world:
- Researchers, scientists and engineers: The Plant Accelerator project at the University of Adelaide solves food supply issues in large automated greenhouses in Europe, Australia and the US, by giving researchers the ability to view thousands of images all at once on a screen, enabling them to spot patterns or anomalies in physiological and chemical traits.
- Data scientists training machine learning models: Cancer Research UK funded project, developing a cancer detection system which identifies cancerous lesions in real-time by highlighting them on the video as the patient is being examined.
- Data integration across silos: Companies such as Siemens, Honeywell and numerous other firms use Zegami to extract insights and answer questions from disparate data sources. This makes the process of data management and data cleaning faster, more efficient and gives them a competitive advantage.