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A RegNetY-1.6GF image classification model. Pretrained on ImageNet-1k by torchvision contributors (see ImageNet1K-V2 weight details https://github.com/pytorch/vision/issues/3995#new-recipe). The `timm` RegNet implementation includes a number of enhancements not present in other implementations, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * configurable output stride (dilation) * configurable activation and norm layers * option for a pre-activation bottleneck block used in RegNetV variant * only known RegNetZ model definitions with pretrained weights - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 11.2 - GMACs: 1.6 - Activations (M): 8.0 - Image size: 224 x 224 - **Papers:** - Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 - **Original:** https://github.com/pytorch/vision

Image classification
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A RegNetY-3.2GF image classification model. Trained on ImageNet-1k by Ross Wightman in `timm`. The `timm` RegNet implementation includes a number of enhancements not present in other implementations, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * configurable output stride (dilation) * configurable activation and norm layers * option for a pre-activation bottleneck block used in RegNetV variant * only known RegNetZ model definitions with pretrained weights - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 19.4 - GMACs: 3.2 - Activations (M): 11.3 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models

Image classification
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The `timm` RegNet implementation includes a number of enhancements not present in other implementations, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * configurable output stride (dilation) * configurable activation and norm layers * option for a pre-activation bottleneck block used in RegNetV variant * only known RegNetZ model definitions with pretrained weights For the comparison summary below, the ra_in1k, ra3_in1k, ch_in1k, sw_*, and lion_* tagged weights are trained in `timm`.

Image classification
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This model architecture is implemented using `timm`'s flexible BYOBNet (Bring-Your-Own-Blocks Network). BYOBNet allows configuration of: * block / stage layout * stem layout * output stride (dilation) * activation and norm layers * channel and spatial / self-attention layers ...and also includes `timm` features common to many other architectures, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * per-stage feature extraction

Image classification
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This model architecture is implemented using `timm`'s flexible BYOBNet (Bring-Your-Own-Blocks Network). BYOBNet allows configuration of: * block / stage layout * stem layout * output stride (dilation) * activation and norm layers * channel and spatial / self-attention layers ...and also includes `timm` features common to many other architectures, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * per-stage feature extraction

Image classification
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This model architecture is implemented using `timm`'s flexible BYOBNet (Bring-Your-Own-Blocks Network). BYOBNet allows configuration of: * block / stage layout * stem layout * output stride (dilation) * activation and norm layers * channel and spatial / self-attention layers ...and also includes `timm` features common to many other architectures, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * per-stage feature extraction

Image classification
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