Codes for PENet: a model for rapid retrieval of natural history collections

Digitalized natural history collections serve as vital ecological and evolutionary research resources. Specimen retrieval based on morphological features allows for the rapid acquisition of similar specimens from these collections, aiding in maximizing the utilization of their resources and catering to the requirements of related research. However, achieving this objective necessitates effective feature extraction and representation techniques. We developed a Phenotype Encoding Network (PENet), a deep learning-based model that combines hashing methods to automatically extract and encode discriminative features into hash codes.We tested the performance of PENet on six datasets, including a newly constructed beetle dataset with six subfamilies and 6 566 images, which covers more than 60% of the genera in the family Scarabaeidae. PENet showed high performance in feature extraction and image retrieval, allowing users to input an image of a specimen and efficiently retrieve all specimens with similar morphology.Two visualization methods, t-SNE, and Grad-CAM, were used to evaluate the representation ability of the hash codes. Further, by using the hash codes generated from PENet, a phenetic distance tree was constructed based on the beetle dataset. The result indicated the hash codes could reveal the phenetic distances and relationships among categories to a certain extent.PENet provides an automatic and efficient method to extract and represent morphological discriminative features. The generated hash code can be used as a low-dimensional carrier of these features, enabling efficient retrieval of specimens. Moreover, the distance information carried by these hash codes suggests their potential in systematics, deserving further exploration.Here are the codes for PENet.