The paper introduces a unified framework called CNN-RNN for multi-label image classification, which combines the strengths of deep convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Traditional methods for multi-label classification often treat each label independently, failing to capture the dependencies between labels. The proposed CNN-RNN framework learns a joint image-label embedding to model both semantic label dependencies and image-label relevance. This end-to-end trained model effectively exploits label co-occurrence dependencies and semantic redundancies, leading to improved classification accuracy. Experimental results on benchmark datasets, including NUS-WIDE, Microsoft COCO, and PASCAL VOC 2007, demonstrate that the proposed method outperforms state-of-the-art multi-label classification models. The framework's ability to adapt image features based on previous predictions and its implicit attention mechanism for small objects are highlighted as key advantages. Additionally, the paper discusses the use of LSTM neurons to model long-term dependencies and the beam search algorithm for efficient multi-label prediction.The paper introduces a unified framework called CNN-RNN for multi-label image classification, which combines the strengths of deep convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Traditional methods for multi-label classification often treat each label independently, failing to capture the dependencies between labels. The proposed CNN-RNN framework learns a joint image-label embedding to model both semantic label dependencies and image-label relevance. This end-to-end trained model effectively exploits label co-occurrence dependencies and semantic redundancies, leading to improved classification accuracy. Experimental results on benchmark datasets, including NUS-WIDE, Microsoft COCO, and PASCAL VOC 2007, demonstrate that the proposed method outperforms state-of-the-art multi-label classification models. The framework's ability to adapt image features based on previous predictions and its implicit attention mechanism for small objects are highlighted as key advantages. Additionally, the paper discusses the use of LSTM neurons to model long-term dependencies and the beam search algorithm for efficient multi-label prediction.