ShuffleNet is an extremely efficient convolutional neural network (CNN) architecture designed for mobile devices with limited computational power. It uses two key operations: pointwise group convolution and channel shuffle, which significantly reduce computational cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection show that ShuffleNet outperforms other structures, achieving a 7.8% lower top-1 error than MobileNet at 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves a 13x speedup over AlexNet while maintaining comparable accuracy. The architecture of ShuffleNet is built on a novel unit that combines pointwise group convolution and channel shuffle. This unit is designed to be efficient and effective for small networks, allowing more feature map channels and thus encoding more information. The ShuffleNet unit is derived from the bottleneck unit, with modifications to incorporate group convolutions and channel shuffle operations. This design enables the network to maintain high accuracy while reducing computational complexity. ShuffleNet is evaluated on ImageNet and MS COCO tasks, showing superior performance compared to other structures. It is also compared with MobileNet and other frameworks, demonstrating its efficiency and effectiveness. The architecture is scalable, with different versions (e.g., ShuffleNet 1x, 0.5x, 2x) that can be adjusted to different computational budgets. The ShuffleNet architecture is also efficient in terms of inference speed, achieving significant speedups on mobile devices. The paper also discusses related work in efficient model design, group convolutions, and channel shuffle operations. It highlights the importance of efficient CNNs for mobile devices and the effectiveness of ShuffleNet in achieving high accuracy with low computational cost. The results show that ShuffleNet is a highly efficient and effective architecture for mobile applications, with significant improvements in performance and speed compared to existing models.ShuffleNet is an extremely efficient convolutional neural network (CNN) architecture designed for mobile devices with limited computational power. It uses two key operations: pointwise group convolution and channel shuffle, which significantly reduce computational cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection show that ShuffleNet outperforms other structures, achieving a 7.8% lower top-1 error than MobileNet at 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves a 13x speedup over AlexNet while maintaining comparable accuracy. The architecture of ShuffleNet is built on a novel unit that combines pointwise group convolution and channel shuffle. This unit is designed to be efficient and effective for small networks, allowing more feature map channels and thus encoding more information. The ShuffleNet unit is derived from the bottleneck unit, with modifications to incorporate group convolutions and channel shuffle operations. This design enables the network to maintain high accuracy while reducing computational complexity. ShuffleNet is evaluated on ImageNet and MS COCO tasks, showing superior performance compared to other structures. It is also compared with MobileNet and other frameworks, demonstrating its efficiency and effectiveness. The architecture is scalable, with different versions (e.g., ShuffleNet 1x, 0.5x, 2x) that can be adjusted to different computational budgets. The ShuffleNet architecture is also efficient in terms of inference speed, achieving significant speedups on mobile devices. The paper also discusses related work in efficient model design, group convolutions, and channel shuffle operations. It highlights the importance of efficient CNNs for mobile devices and the effectiveness of ShuffleNet in achieving high accuracy with low computational cost. The results show that ShuffleNet is a highly efficient and effective architecture for mobile applications, with significant improvements in performance and speed compared to existing models.