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The application of machine vision to the automatic detection of surface defects on magnetic sheets can increase production efficiency and reduce production costs. Deep convolutional neural networks have high-precision classification performance, especially in image recognition. However, the deep neural network model proposed so far cannot meet the requirements of real-time detection in the industrial production line due to the huge amount of parameters and computation. To solve this problem, based on deep separable convolution and channel shuffling, we proposed a lightweight, high-efficiency and low-latency convolutional neural network architecture called MagnetNets.In order to evaluate the performance of the MagnetNets network model, we compared it with MobileNets, ShuffleNet, Xception, and MobileNetV2 in the public dataset ImageNet.And then the MagnetNets network model is applied to the defect detection system for magnetic defect detection.The experimental results show that the proposed network architecture significantly reduces the number of parameters and has good performance,At the same time, the delay is reduced and the detection speed is improved in the disk defect detection system and the defect detection recognition rate reaches 97.3%.