Discover how Data Explorer's image-based search and patch search simplify dataset curation for computer vision tasks. Find relevant data quickly and efficiently.
In this blog, we will see how to automatically select a subset of images for training.
Data Explorer manipulates videos, modifies frame rate for faster processing, splits them into scenes and allows for further curation and exploration.
We see how to perform an image based search or a patch based search in a video, and increase the chances of finding results in different scenes.
Learn how Data Explorer simplifies dataset curation for computer vision tasks. Filter and visualize metadata to enhance algorithm and model development.
Use Data Explorer in the Kaggle RSNA Mammography competition or EDA and coreset selection
As new uses for cameras and the visual data created become available, the need for tools to find the most valuable subsets becomes essential.
This article was originally published on InsideBigData. It is time to shift from a model-centric mindset to a data-centric approach. AI is a massive part of human life today and is woven into the fabric of our everyday society. From medical imagery scanning to the ubiquity of facial recognition software in our cell phones, AI […]
The challenge Imagine you’ve just been given a new batch of 10,000 images or hours of video and you need to find only a small portion of relevant images. How would you go about isolating that subset of the data? In many cases, we receive a batch of visual data, images or video, with very […]
ECCV 2022 was a truly remarkable event. With two full days of incredible workshops and tutorials, followed by three additional days of the main conference, Computer Vision was celebrated in every corner. During the conference, giants like Amazon, Google and Meta, giants-in-the-making like Akriadata, and a whole host of awesome companies gathered to present intriguing […]