  Vision transformers are nowadays the de-facto choice for image classification
tasks. There are two broad categories of classification tasks, fine-grained and
coarse-grained. In fine-grained classification, the necessity is to discover
subtle differences due to the high level of similarity between sub-classes.
Such distinctions are often lost as we downscale the image to save the memory
and computational cost associated with vision transformers (ViT). In this work,
we present an in-depth analysis and describe the critical components for
developing a system for the fine-grained categorization of plants from
herbarium sheets. Our extensive experimental analysis indicated the need for a
better augmentation technique and the ability of modern-day neural networks to
handle higher dimensional images. We also introduce a convolutional transformer
architecture called Conviformer which, unlike the popular Vision Transformer
(ConViT), can handle higher resolution images without exploding memory and
computational cost. We also introduce a novel, improved pre-processing
technique called PreSizer to resize images better while preserving their
original aspect ratios, which proved essential for classifying natural plants.
With our simple yet effective approach, we achieved SoTA on Herbarium 202x and
iNaturalist 2019 dataset.
