CLIP-Adaptive Visual Inspection Framework for Age Recognition of Pericarpium Citri Reticulatae

The accurate identification of the storage age of pericarpium citri reticulatae (PCR) is crucial for assessing its medicinal value. However, the existing approaches face two key limitations: 1) the feature representation ability of the backbone is often insufficient due to the limited data and domain gap and 2) full fine-tuning of the backbone and complex model designs lead to increased training costs and suboptimal performance. To address these issues, a CLIP -adaptive visual inspection framework is developed, consisting of two crucial components: a CLIP-based feature extraction module and an adaptive decision module. Specifically, the former utilizes a frozen CLIP backbone to extract high-level semantic representations. The latter addresses the high training costs and performance limitations of the model through a meticulously designed adaptive decision module. Extensive experiments on two PCR age recognition datasets demonstrate the effectiveness of the proposed approach, achieving 99.84% (98.67%) accuracy, 99.85% (98.67%) precision, 99.84% (98.67%) recall, and 99.84% (98.66%) ${F}1$ score, respectively. Furthermore, the proposed method reduces the number of trainable parameters by a factor of 21, significantly outperforming the state-of-the-art methods. The source code and models will be publicly available at https://github.com/JiaMingZhong2621/PCR