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| library_name: pytorch | |
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| 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](https://arxiv.org/abs/2104.00298) | |
| # 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](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_v2_s.html) | |
| - Original Weight: [EfficientNet_V2_S_Weights.IMAGENET1K_V1](https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth) | |
| - Resolution: 3x384x384 | |
| - Support Cooper version: | |
| - Cooper SDK: [2.5.2] | |
| - Cooper Foundry: [2.2] | |
| | Model | Device | Model Link | | |
| | :-----: | :-----: | :-----: | | |
| | EfficientNetV2 | N1-655 | [Model_Link](https://huggingface.co/Ambarella/EfficientNetV2/blob/main/n1-655_efficientnet_v2_s.bin) | | |
| | EfficientNetV2 | CV72 | [Model_Link](https://huggingface.co/Ambarella/EfficientNetV2/blob/main/cv72_efficientnet_v2_s.bin) | | |
| | EfficientNetV2 | CV75 | [Model_Link](https://huggingface.co/Ambarella/EfficientNetV2/blob/main/cv75_efficientnet_v2_s.bin) | |