16.1 The winning solution

A detailed description of the winning solution is given at https://www.kaggle.com/c/petfinder-pawpularity-score/discussion/301686

The team consisted of:

• Giba
• Competition grandmaster

Giba`s description is as follows:

Giba`s description is as follows:

So basically the challenge is a regression and we were asked to predict the Pawpularity of each pet given the image and some tabular meta features.

The solution consists of two parts

• PART 1. Transfer Learning + SVR
  • The idea was to extract features from pretrained architectures to transform the problem into a tabular approach, then fit a model using that data. To extract features from images he used imagenet pretrained models available in timm and OpenAI CLIP libraries.
• PART 2. CNN + Vision Transformers ensembling:
  • This part of solution is an weighted average of 5 classical image regressions models using different backbones and augmentations.