The model design incorporates a compact architecture utilizing depthwise separable convolutions to minimize parameters and FLOPs, inverted residual blocks (inspired by MobileNetV2) to balance depth and width efficiently, and channel reduction techniques to eliminate redundancy while maintaining expressiveness.