EfficientNetV2 proposes improved scaling rules and training-aware architectural optimizations, including fused-MBConv blocks, to achieve faster training and better accuracy–efficiency trade-offs than prior EfficientNet models.

Original paper: EfficientNetV2: Smaller Models and Faster Training

EfficientNetV2-S

This model uses the EfficientNetV2-S variant, a compact configuration that balances accuracy, inference latency, and training speed. It is well suited for production image classification and as a backbone in vision pipelines where fast convergence and efficient deployment are important.

Model Configuration:

• Reference implementation: torchvision.models.efficientnet_v2_s
• Original Weight: EfficientNet_V2_S_Weights.IMAGENET1K_V1
• Resolution: 3x384x384
• Support Cooper version:
  • Cooper SDK: [2.5.2]
  • Cooper Foundry: [2.2]

Model	Device	Model Link
EfficientNetV2	N1-655	Model_Link
EfficientNetV2	CV72	Model_Link
EfficientNetV2	CV75	Model_Link