MobileNetV2 is a convolutional neural network architecture designed to perform well on mobile devices. It is based on an inverted residual structure, where the residual connections occur between the bottleneck layers. The intermediate expansion layer employs lightweight depth wise convolutions to filter features, serving as a source of non-linearity.
It aims to strike a balance between model performance and efficiency, making it suitable for resource-constrained devices. These lightweight convolutions reduce computational complexity while maintaining expressive power. MobileNetV2 has been used for tasks such as image classification, object detection, and semantic segmentation. It is a powerful neural network architecture optimized for mobile deployment, making it an excellent choice for various vision tasks on handheld devices.
MobileNetV2 models are faster while maintaining the same accuracy across various latency levels. They use 2x fewer operations and require 30% fewer parameters compared to MobileNetV1. Both MobileNetV1 and MobileNetV2 utilize depth wise separable convolutions for efficiency. However, MobileNetV2 introduces inverted residual blocks with bottlenecking features.
MobileNetV2 consists of an initial fully convolutional layer with 32 filters, followed by 19 residual bottleneck layers. MobileNetV2 has a drastically lower parameter count than the original MobileNet and supports any input size greater than 32 X 32, with larger image sizes offering better performance.
MobileNetV2 expands the channels in its separable convolutions, providing more information to process. The channel squeezing part in MobileNetV2 is linear, leading to improved performance.