A dataset of images, used for computer vision tasks, could be the key to success or failure. A clean dataset could lead the way to a great algorithm and model
A visual dataset used for computer vision tasks, could be the key to success or failure. So how do you make sure your algorithm and model are based on strong foundations?
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 […]