This repository contains a 1st place solution for the Painter by Numbers competition on Kaggle. Below is a brief description of the dataset and approaches I've used to build and validate a predictive model. The challenge of the competition was to examine pairs of paintings and determine whether they were painted by the same artist. The training set consists of artwork images and their corresponding class labels (painters). Examples in the test set were split into 13 groups and all possible pairs within each group needed to be examined for the submission.

Does every painter leave a fingerprint? Accurately distinguishing the artwork of a master from a forgery can mean a difference in millions of dollars. In the Painter by Numbers playground competition hosted by Kiri Nichol (AKA small yellow duck), Kagglers were challenged to identify whether pairs of paintings were created by the same artist. In this winner's interview, Nejc Ilenič takes us through his first place solution to this painter recognition challenge. His combination of unsupervised and supervised learning methods helped him achieve a final AUC of 0.9289.