Model card for nfnet l0.ra2 in1k A NFNet Lite (Lightweight NFNet) image classification model. Trained in timm by Ross Wightman. Normalization Free Networks are (pre activation) ResNet like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non linearities based on signal propagation analysis. Lightweight NFNets are timm specific variants that reduce the SE and bottleneck ratio from 0.5 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU. Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 35.1 GMACs: 4.4 Activations (M): 10.5 Image size: train = 224 x 224, test = 288 x 288 Papers: High Performance Large Scale Image Recognition Without Normalization: https://arxiv.org/abs/2102.06171 Characterizing signal propagation to close the performance gap in unnormalized ResNets: https://arxiv.org/abs/2101.08692 Original: https://github.com/huggingface/pytorch image models Dataset: ImageNet 1k Model Usage Image Classification Feature Map Extraction…
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