Our Semantic Search Engine for unlabeled image and video data helps you understand and improve your data in minutes instead of weeks. You can explore the data distribution, data redundancies, outliers and potential for enrichment or augmentation on a dataset level. Based on this transparency, you can reduce and refine datasets and improve data distribution for labeling, annotation, model training and online learning.
Search. Refine. Extract.
Meet Quasara Plato
Understand your raw data with Plato Semantic Clustering.
Manually preparing meaningful training and validation datasets based on large image datasets with hundreds of thousands or even millions of raw images is hard, painful and, let‘s face it, boring.
With Plato Clustering, computer vision engineers are able to quickly get dataset-level insights into #datadistribution, potential #dataredundancy and a quick overview of individual datapoints.
Based on this data transparency, Quasara Plato helps you drill down into the dataset and extract the most useful subset you need.
Find the needle in the haystack with Plato Outlier Detection.
Great data changes everything:
- Uncover data distribution, domain gaps and biases in your datasets.
- Save up to 50% of data labeling and annotation costs.
- Reduce the raw dataset size by up to 90% compared to the raw data collected.
- Save up to 80% of the time spent on manual dataset preparation and curation.
- Increase success rate of Computer Vision projects.
Want to know what you can find out with Quasara Plato?
Download a free copy of our sample report here!
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Quasara started its journey at the Technical University of Munich where all three founders studied. Being a Munich-based startup at heart, we are a distributed team working across Europe.
We are on a mission to expedite the adoption of Computer Vision and NLP -powered products that change the world. We do that by making data-centric AI possible with the Semantic Search Engine that we built.
Preparing high quality datasets for real-world AI is at the heart of what we do. To make this happen, one needs to understand raw datasets and thus be able to explore the data before the next step for labeling, annotation, model training or online learning is made. We believe that it is time for an easier and better way to develop, launch and maintain Computer Vision models.