  With fine-grained classification, we identify unique characteristics to
distinguish among classes of the same super-class. We are focusing on species
recognition in Insecta, as they are critical for biodiversity monitoring and at
the base of many ecosystems. With citizen science campaigns, billions of images
are collected in the wild. Once these are labelled, experts can use them to
create distribution maps. However, the labelling process is time-consuming,
which is where computer vision comes in. The field of computer vision offers a
wide range of algorithms, each with its strengths and weaknesses; how do we
identify the algorithm that is in line with our application? To answer this
question, we provide a full and detailed evaluation of nine algorithms among
deep convolutional networks (CNN), vision transformers (ViT), and
locality-based vision transformers (LBVT) on 4 different aspects:
classification performance, embedding quality, computational cost, and gradient
activity. We offer insights that we haven't yet had in this domain proving to
which extent these algorithms solve the fine-grained tasks in Insecta. We found
that the ViT performs the best on inference speed and computational cost while
the LBVT outperforms the others on performance and embedding quality; the CNN
provide a trade-off among the metrics.
