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A RegNetX-200MF image classification model. Pretrained on ImageNet-1k by paper authors. 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): 2.7 - GMACs: 0.2 - Activations (M): 2.2 - Image size: 224 x 224 - **Papers:** - Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 - **Dataset:** ImageNet-1k - **Original:** https://github.com/facebookresearch/pycls

Image classification
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A RegNetY-200MF image classification model. Pretrained on ImageNet-1k by paper authors. 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): 3.2 - GMACs: 0.2 - Activations (M): 2.2 - Image size: 224 x 224 - **Papers:** - Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 - **Dataset:** ImageNet-1k - **Original:** https://github.com/facebookresearch/pycls

Image classification
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A RegNetY-400MF 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 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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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
Input Image→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗
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