Enhanced Knowledge Distillation for Advanced Recognition of Chinese Herbal Medicine.
Sensors (Basel)
; 24(5)2024 Feb 28.
Article
em En
| MEDLINE
| ID: mdl-38475094
ABSTRACT
The identification and classification of traditional Chinese herbal medicines demand significant time and expertise. We propose the dual-teacher supervised decay (DTSD) approach, an enhancement for Chinese herbal medicine recognition utilizing a refined knowledge distillation model. The DTSD method refines output soft labels, adapts attenuation parameters, and employs a dynamic combination loss in the teacher model. Implemented on the lightweight MobileNet_v3 network, the methodology is deployed successfully in a mobile application. Experimental results reveal that incorporating the exponential warmup learning rate reduction strategy during training optimizes the knowledge distillation model, achieving an average classification accuracy of 98.60% for 10 types of Chinese herbal medicine images. The model boasts an average detection time of 0.0172 s per image, with a compressed size of 10 MB. Comparative experiments demonstrate the superior performance of our refined model over DenseNet121, ResNet50_vd, Xception65, and EfficientNetB1. This refined model not only introduces an approach to Chinese herbal medicine image recognition but also provides a practical solution for lightweight models in mobile applications.
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Base de dados:
MEDLINE
Assunto principal:
Medicamentos de Ervas Chinesas
/
Aplicativos Móveis
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article