Recognition method for fine-grained product styles based on deep learning
To effectively extract product style features with differences,a Fine-grained Styles Recognition Convolutional Neural Network(FSR-CNN)based on composite learning pipelines was proposed,which simulation integrated two key learning mechanisms of the human brain nervous system.From the attention learning pipeline,based on the residual struc-ture,the coordinate attention,convolutional block attention and multi-head attention were embedded in a string-parallel combination to form a lightweight Hybrid Attention Residual Network(HA-ResNet)for extracting"specialized features".From the transfer learning pipeline,the fine-tuning pre-trained GoogLeNet was used to expand the capacity of HA-ResNet model for extracting multi-receptive field"generic features".Finally,the output features of both were fused and the MLP classifier was used to identify the product style types.Experiments were performed on a self-built bicycle helmet dataset and compared with other classical deep convolutional neural network models.The experimental results showed that the FSR-CNN model exhibited higher accuracy and stronger robustness,which provided a new model algorithm architecture for prod-uct styles fine retrieval and reuse.
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