Effectively analyzing， processing and accurately classifying epileptic electroencephalographic （EEG） signals can further improve the problem of epilepsy detection. Therefore， various deep learning approaches have been gradually applied to this problem， such as using the BiLSTM model to process the 1D time series data of epileptic EEG. To further improve the accuracy of epileptic EEG classification， the 1D time series data of epileptic EEG is converted into 2D images and the EfficientNetV2 model is used to achieve binary classification for epilepsy detection in this paper. At the same time， the gradient-weighted class activation mapping （Grad-CAM） is introduced for visual analysis of 2D images classification. By performing classification experiments on a pre-processed version of the epilepsy EEG signal dataset from the University of Bern， Germany， the EfficientNetV2 model achieves the accuracy of 98.69%， which is better than the BiLSTM model. The result indicates that the EfficientNetV2 model can effectively achieve epileptic EEG classification by 2D EEG images with higher classification accuracy.