1 code implementation • 6 Oct 2021 • Connor Anderson, Ryan Farrell
Challenging issues with large-scale image datasets become points of elegance for fractal pre-training: perfect label accuracy at zero cost; no need to store/transmit large image archives; no privacy/demographic bias/concerns of inappropriate content, as no humans are pictured; limitless supply and diversity of images; and the images are free/open-source.
no code implementations • 7 Sep 2021 • Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell
From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.
no code implementations • 23 Jun 2020 • Connor Anderson, Matt Gwilliam, Adam Teuscher, Andrew Merrill, Ryan Farrell
In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy.