基于长短期记忆网络与MobileNet V2的图像分类研究OA

An image classification framework based on MobileNet V2 and long and short-term memory(LSTM)network is proposed,which aims to obtain more efficient and accurate image classification results.MobileNet V2 is used to classify images,and the LSTM network is used to enhance the performance of the model by maintaining the feature state information encountered in the previous process of image classification.The attention mechanism can be easily integrated into the standard convolutional neural network(CNN)architecture to improve the model accuracy and prediction accuracy at the expense of the minimum computing load.Therefore,this attention mechanism is adopted to improve the whole MobileNet V2 model to achieve the desired effect.The CIFAR-10 and CIFAR-100 datasets are used for classification tasks in order to verify the performance of the model.Experiments show that the proposed model effectively reduces the computational complexity and required parameters compared with other CNN architectures on CIFAR-10 and CIFAR-100 datasets.To sum up,the proposed model can promote the application and development of related technologies in multiple fields.