E-ConvNeXt combines CSPNet, batch-normalized ConvNeXt blocks, a stepped stem, and ESE attention to reach 78.3-81.9% ImageNet top-1 at 0.9-3.1 GFLOPs.
Batch renormalization: Towards reducing minibatch dependence in batch-normalized models.Advances in neural infor- mation processing systems, 30, 2017
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E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections
E-ConvNeXt combines CSPNet, batch-normalized ConvNeXt blocks, a stepped stem, and ESE attention to reach 78.3-81.9% ImageNet top-1 at 0.9-3.1 GFLOPs.