This year, Carvana, a successful online used car startup, challenged the Kaggle community to develop an algorithm that automatically removes the photo studio background. This would allow Carvana to superimpose cars on a variety of backgrounds.

In this winner’s interview, the first place team of accomplished image processing competitors named Team Best[over]fitting, shares in detail their winning approach. As it often happens in the competitions, we never met in person, but we knew each other pretty well from the fruitful conversations about Deep Learning held on the Russian-speaking Open Data Science community, ods.ai.

Although we participated as a team, we worked on 3 independent solutions until merging 7 days before the end of the competition. Each of these solutions were in the top 10–Artsiom and Alexander were in 2nd place and Vladimir was in 5th.

Our final solution was a simple average of three predictions. You can also see this in the code that we prepared for organizers and released on GitHub–there are 3 separate folders:

Each of us spent about two weeks on this challenge, although to fully reproduce our solution on a single Titan X Pascal one would need about 90 days to train and 13 days to make predictions. Read more from blog.kaggle.com…

thumbnail courtesy of kaggle.com