HindwingLib: A library of leaf beetle hindwings generated by Stable Diffusion and ControlNet
Abstract The utilization of datasets from beetle hindwings is prevalent in research of morphology and evolution of beetles, serving as a valuable tool for comprehending the evolutionary processes and functional adaptations under specific environmental conditions. However, the collection of hindwing...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05010-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract The utilization of datasets from beetle hindwings is prevalent in research of morphology and evolution of beetles, serving as a valuable tool for comprehending the evolutionary processes and functional adaptations under specific environmental conditions. However, the collection of hindwing images of beetles poses several challenges, including limited sample availability, complex sample preparation procedures, and restricted public accessibility. Recently, a machine learning technique called Stable Diffusion has been developed to statistically generate diverse images using a pretrained model with prompts. In this study, we introduce an approach utilizing Stable diffusion and ControlNet for the generation of beetle hindwing images, along with the corresponding results obtained from its application to a diverse set of 200 leaf beetle hindwings. To demonstrate the fidelity of the synthetic hindwing images, we conducted a comprarative analysis of three key metrics: Structural Similarity Index (SSIM), Inception Score (IS), and Fréchet Inception Distance (FID), which are crucial for evaluating image fidelity. The results demonstrated a strong alignment between the actual data and the synthetic images, confirming their high fidelity. This novel library of leaf beetle hindwings not only offers morphological image for utilization in machine learning, but also showcases the extensive applicability of the proposed methodology. |
|---|---|
| ISSN: | 2052-4463 |