Abstract
This paper proposes a novel image classification architecture named ensemble cross-stage partial attention network based on the backbone network DarkNet53 of Yolov3 to improve the feature extraction capability and the interpretability of image classification. This network has multiple advantages, including light model parameters, fast classification speed, and high classification accuracy for small objects and complex images. Local network architectures of different cross-phases are added in the proposed network structure to reduce the calculation. Furthermore, channel and hybrid domain attention modules, which, respectively, fuse the branch feature with the extracted channel and spatial attention features, are designed for feature extraction of images. Experimental results confirm the improved performance of the proposed approach on the CIFAR-100, ImageNet, and UCMerced datasets. In addition, experiments on the MSCOCO dataset suggest the application of the proposed method to object detection with satisfactory accuracy.
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