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HindwingLib:由稳定扩散和控制网生成的叶甲虫后翼图书馆

Yi Yang^1,2, WenJie Li^1,3, RuiZe Liu⁴

¹Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing, 100101, China.

Scientific data

|April 23, 2025

概括

研究人员使用稳定扩散和控制网生成了高保真甲虫后翼图像,克服了数据限制. 这一新数据集有助于进化形态学研究和机器学习应用.

科学领域:

昆虫学昆虫学是一门学科.
进化生物学进化生物学
计算机视觉计算机视觉

背景情况:

甲虫后翼数据集对于研究甲虫形态和演变至关重要.
目前的后翼图像收集面临的挑战包括有限的样本,复杂的准备和可访问性问题.

研究的目的:

通过使用稳定扩散和ControlNet.Net,引入一种用于生成多样化和高保真性甲虫后翼图像的新方法.
为了解决获取和访问甲虫后翼图像数据的局限性,用于研究.

主要方法:

利用稳定扩散和控制网,一种机器学习技术,用于图像生成.
应用了该方法来生成200种不同的叶甲虫后翼的图像.
使用结构相似性指数 (SSIM),初始分数 (IS) 和Fréchet初始距离 (FID) 进行了比较分析,以评估图像保真度.

主要成果:

生成的合成后翼图像显示了高保真度,显示与真实的甲虫后翼数据有很强的对齐.
定量指标 (SSIM,IS,FID) 证实了合成图像的质量和真实性.
一个新的图书馆的叶甲虫后翼图像被创建.

结论:

拟议的稳定扩散和控制网方法有效生成高保真甲虫后翼图像.
这种方法克服了与传统图像收集相关的挑战,为机器学习和进化研究提供了宝贵的资源.
生成的图书馆扩大了甲虫的形态数据可用性.

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