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is paper is devoted to a lightweight convolutional neural network based on the attention mechanism called the tiny attention network (TANet). e TANet consists of three main parts termed as a reduction module, self-attention operation, and group convolution. e reduction module alleviates information loss caused by the pooling operation. e new parameter-free selfattention operation makes the model to focus on learning important parts of images. e group convolution achieves model compression and multibranch fusion. Using the main parts, the proposed network enables efficient plankton classification on mobile devices. e performance of the proposed network is evaluated on the Plankton dataset collected by Oregon State University's Hatfield Marine Science Center. e results show that TANet outperforms other deep models in speed (31.8 ms per image), size (648 kB, the size of the hard disk space occupied by the model), and accuracy (Top-1 76.5%, Top-5 96.3%).