We present a simple multi-scale learning network for image classification that is inspired by the MobileNet. The proposed method has two advantages: (1) It uses the multi-scale block with depthwise separable convolutions, which forms multiple sub-networks by increasing the width of the network while keeping the computational resources constant. (2) It combines the multi-scale block with residual connections and that accelerates the training of networks significantly. The experimental results show that the proposed method has strong performance compared to other popular models on different datasets.

中文翻译：

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具有深度可分离卷积的多尺度学习网络

我们介绍了一个受MobileNet启发的简单的多尺度学习网络，用于图像分类。所提出的方法有两个优点：（1）它使用具有深度可分离卷积的多尺度块，通过在保持计算资源不变的情况下增加网络的宽度来形成多个子网。（2）将多尺度块与剩余连接相结合，大大加快了网络的训练速度。实验结果表明，与其他流行模型相比，该方法在不同数据集上具有较强的性能。