Multi-label image classification aims to predict multiple labels for a single image which consists of diverse contents. The main challenge in Multi-label classification task to achieve a decent performance is the lack of enough training data. Convolutional Neural Networks (CNN) has shown satisfying results in single-label image classification, but multi-label image classification is still an open field of research. In this paper an efficient hybrid method for multi-label image classification is proposed. The proposed model consists of multiple sub-networks. The experimental results obtained in this study demonstrate the plausible performance of the proposed method on ”Pascal VOC 2012” and ”Kaggle: Understanding the Amazon from space challenge” datasets.
