Aiming at the limited feature extraction capability and inefficient quantization constraint mechanism of existing hashing algorithms, a deep multi-scale attention hashing network was proposed for large-scale image retrieval. The whole network was composed of a main branch and a object branch. In the main branch, two modules of multi-scale attention localization and saliency region extraction were added to effectively localize and extract saliency regions of images, and the results were fed into the object branch to learn more detailed features. Subsequently, the multi-granularity features learned by two branches were fused to perform binary hash coding. In addition, a triplet quantization constraint was introduced to reduce quantization error while maintaining the similarity relationship between sample pairs. In order to verify the effectiveness of the proposed method, extensive experiments were carried out on two benchmark datasets. Experimental results showed that our method outperforms most existing hashing retrieval approaches.