<p>An efficient and accurate model for recognizing fine-grained clothing attributes has significant commercial potential and social impact. However, the inherent diversity and complexity of clothing make acquiring datasets with fine-grained attributes a costly endeavor. To address these challenges, we propose a lightweight clothing fine-grained attributes recognition model. First, the Ghost module is introduced into the CSPDarknet network to enhance the depth and expressiveness of feature learning while reducing the parameters and computational complexity. Then, the Conv module is replaced by the GSConv module in the PAFPN network to further reduce the network computational load, and the SE attention mechanism is also added to enhance key feature perception. Finally, the Detect module is employed to effectively recognize fine-grained clothing attributes. To evaluate performance, we constructed a clothing dataset containing 20 fine-grained attributes. Experimental results show that the model achieves precision, recall and mAP of 76.2%, 78.9% and 81.7%. Compared to the original model, the number of parameters is reduced by 26.2%, and the FPS improves by 25.4%. Our proposed model performs well on a small-scale dataset and improves performance in resource-constrained environments, making it highly applicable to clothing recommendation, virtual fitting, and personalization.</p>