  The vision transformer (ViT) has advanced to the cutting edge in the visual
recognition task. Transformers are more robust than CNN, according to the
latest research. ViT's self-attention mechanism, according to the claim, makes
it more robust than CNN. Even with this, we discover that these conclusions are
based on unfair experimental conditions and just comparing a few models, which
did not allow us to depict the entire scenario of robustness performance. In
this study, we investigate the performance of 58 state-of-the-art computer
vision models in a unified training setup based not only on attention and
convolution mechanisms but also on neural networks based on a combination of
convolution and attention mechanisms, sequence-based model, complementary
search, and network-based method. Our research demonstrates that robustness
depends on the training setup and model types, and performance varies based on
out-of-distribution type. Our research will aid the community in better
understanding and benchmarking the robustness of computer vision models.
