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?
Data Explorer allows you to search the dataset for images that are similar to a certain image. Searching is performed based on the feature vector of each image with no metadata required.
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.
Data Explorer is a platform that was built to allow us focus on the data, curate it, clean it and make sure we start the development cycles with a great foundation.
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.
Stop Building Models, Start Training Data 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 […]