  In this paper, we compare Model Soups performances on three different models
(ResNet, ViT and EfficientNet) using three Soup Recipes (Greedy Soup Sorted,
Greedy Soup Random and Uniform soup) from arXiv:2203.05482, and reproduce the
results of the authors. We then introduce a new Soup Recipe called Pruned Soup.
Results from the soups were better than the best individual model for the
pre-trained vision transformer, but were much worst for the ResNet and the
EfficientNet. Our pruned soup performed better than the uniform and greedy
soups presented in the original paper. We also discuss the limitations of
weight-averaging that were found during the experiments. The code for our model
soup library and the experiments with different models can be found here:
https://github.com/milo-sobral/ModelSoup
