News, insight and updates from the world of FoxyAI.
FoxyAI continuously develops, audits, and updates models as an integral part of its research operations. This document outlines FoxyAI’s scientific process and highlights results from our most recent models, validating our commitment to data-driven methodologies.
Dive into the balance between precision & recall in AI, essential for optimal model performance.
Explore vector databases and how they turn unstructured data into searchable vectors for easy similarity searches in images and text.
With the widespread adoption of Artificial Intelligence, organizations have access to unprecedented amounts of data, insights, and hidden patterns that can be extremely valuable to any organization that can leverage it.
To prevent FoxyAI’s customers’ servers from idly waiting, or actively polling our API, we’ve created an asynchronous approach using webhooks.
When developing ML models, there is a continuous feedback loop in order to improve their performance.
Training a visual machine learning model first involves labeling a set of images. The labels indicate what is present in an image, and in some cases, its bounding box or polygon.
When viewing photos and/or visual media of a property, there is an abundance of knowledge inherent in the photos that can be learned about the space. For example, it can be determined if there is a bathtub, the state (condition) of the kitchen, or if there are hardwood floors in the living room.
At FoxyAI we believe that just as a home buyer scrupulously inspects the visual quality of a property, so should the machines.
