In this paper, an enhanced version of interpretable optimization-inspired deep network for image compressive sensing(ISTA-Net) based on hybrid precision training, dubbed Mix-ISTA, ensures high compressed reconstruction and speed up training. ISTA-Net is a deep neural network architecture whose design is inspired by the Iterative Shrinkage Threshold Algorithm (ISTA), which has the advantages of both optimization and network. However, the training process for ISTA-Net can be quite time-consuming when trained on large-scale datasets. In order to overcome this problem, we introduce a hybrid precision training technology, which can effectively consume memory and compute costs by combining single-precision (FP32) and half-precision (FP16) operations. The experimental results show that the training speed is significantly improved after mixed precision training, while the reconstruction performance of ISTA-Net is not affected. While greatly speeding up the training, it can still achieve high reconstruction results, so that it has more practical value in practical applications.