MobileNetV2 introduces an efficient convolutional architecture based on inverted residual blocks and linear bottlenecks, enabling high accuracy at very low computational cost for mobile and embedded vision applications.

Original paper: MobileNetV2: Inverted Residuals and Linear Bottlenecks

MobileNetV2

This model uses the MobileNetV2 architecture with width multiplier = 1.0, providing a balanced trade-off between accuracy and efficiency. It is optimized for low-latency, low-power inference and is commonly used for on-device image classification as well as a backbone for mobile-friendly detection and segmentation models.

Model Configuration:

• Reference implementation: MobileNetV2
• Original Weight: MobileNet_V2_Weights.IMAGENET1K_V2
• Resolution: 3x224x224
• Support Cooper version:
  • Cooper SDK: [2.5.2]
  • Cooper Foundry: [2.2]

Model	Device	Model Link
MobileNetV2	N1-655	Model_Link
MobileNetV2	CV72	Model_Link
MobileNetV2	CV75	Model_Link