The traditional algorithm for compressive reconstruction has high computational complexity. In order to reduce the reconstruction time of compressive sensing, deep learning networks have proven to be an effective solution. In this paper, we have developed a single-pixel imaging system based on deep learning and designed the binary sampling Res2Net reconstruction network (Bsr2-Net) model suitable for binary matrix sampling. In the experiments, we compared the structural similarity, peak signal-to-noise ratio, and reconstruction time using different reconstruction methods. Experimental results show that the Bsr2-Net is superior to several deep learning networks recently reported and closes to the most advanced reconstruction algorithms.