Automatic　image　annotation　is　a　key　step　in　image　retrieval　and　image　understanding.　In　this　paper,　we　present　an　end-to-end　automatic　image　annotation　method　based　on　a　deep　convolutional　neural　network　(CNN)　and　multi-label　data　augmentation.　Different　from　traditional　annotation　models　that　usually　perform　feature　extraction　and　annotation　as　two　independent　tasks,　we　propose　an　end-to-end　automatic　image　annotation　model　based　on　deep　CNN　(E2E-DCNN).　E2E-DCNN　transforms　the　image　annotation　problem　into　a　multi-label　learning　problem.　It　uses　a　deep　CNN　structure　to　carry　out　the　adaptive　feature　learning　before　constructing　the　end-to-end　annotation　structure　using　multiple　cross-entropy　loss　functions　for　training.　It　is　difficult　to　train　a　deep　CNN　model　using　small-scale　datasets　or　scale　up　multi-label　datasets　using　traditional　data　augmentation　methods;　hence,　we　propose　a　multi-label　data　augmentation　method　based　on　Wasserstein　generative　adversarial　networks　(ML-WGAN).　The　ML-WGAN　generator　can　approximate　the　data　distribution　of　a　single　multi-label　image.　The　images　generated　by　ML-WGAN　can　assist　in　the　reduction　of　the　over-fitting　problem　of　training　a　deep　CNN　model　and　enhance　the　generalization　ability　of　the　trained　CNN　model.　We　optimize　the　network　structure　by　using　deformable　convolution　and　spatial　pyramid　pooling.　We　experiment　the　proposed　E2E-DCNN　model　with　data　augmentation　by　the　proposed　ML-WGAN　on　several　public　datasets.　The　experimental　results　demonstrate　that　the　proposed　model　outperforms　the　state-of-the-art　automatic　image　annotation　models.