宁夏大学学报（自然科学版）2024，Vol.45Issue(3)：307-314,8.
面向社会性昆虫识别的知识迁移DenseNet后训练剪枝轻量化模型研究
Research on Lightweight Model of Post-training Pruning of Knowledge-transferred DenseNet for Social Insects Identification
摘要
Abstract
The rapid and effective identification of populations such as bees and ants,which have significant eco-logical impacts,on resource-constrained devices holds great ecological conservation significance.In this paper,a DenseNet pre-trained model is employed for knowledge transfer on a small-scale dataset of ants and bees,and an unstructured post-training pruning method known as UPSCALE is utilized to construct a comprehensive framework.Experimental results demonstrate that this framework can rapidly leverage small-scale datasets to achieve target image recognition with high accuracy,while the model parameters are less than one-third of those of the baseline method,thus providing broader application value for deployment on devices.关键词
蚂蚁/蜜蜂/DenseNet/迁移学习/后训练剪枝Key words
ants/bees/DenseNet/transfer learning/post-training pruning分类
计算机与自动化引用本文复制引用
王鑫,张文静,史伟,可乐乐..面向社会性昆虫识别的知识迁移DenseNet后训练剪枝轻量化模型研究[J].宁夏大学学报（自然科学版）,2024,45(3):307-314,8.基金项目
国家自然科学基金资助项目(62166030 （）
12061055) （）
甘肃省自然科学基金资助项目(23JRRA1471) （23JRRA1471）
