MixNet (Mixed Depthwise Convolutional Networks)

MixNet is a family of mobile-scale convolutional neural networks that utilize Mixed Depthwise Convolutions (MDConv) to achieve superior efficiency and accuracy. Developed by Google Research, MixNet addresses the limitations of standard depthwise convolutions by mixing multiple kernel sizes (e.g., 3x3, 5x5, 7x7) within a single convolution operation. This architecture allows the model to capture high-resolution patterns and low-resolution context simultaneously without the massive parameter overhead of traditional ensembles. When applied to semantic segmentation tasks—often integrated with heads like DeepLabV3+ or Lite-RASP—MixNet provides a lightweight yet powerful backbone that outperforms MobileNetV3 and MnasNet. In the 2026 market, MixNet remains a critical reference architecture for edge-based AI, particularly in autonomous systems and real-time mobile applications where compute budgets are constrained. Its technical architecture is specifically tuned for hardware accelerators that support grouped convolutions, making it a preferred choice for developers building on Snapdragon, Apple Silicon, and Google Tensor chips.