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A Swin Transformer image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 49.6 - GMACs: 8.8 - Activations (M): 27.5 - Image size: 224 x 224 - **Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Swin Transformer image classification model. Pretrained on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.3 - GMACs: 4.5 - Activations (M): 17.1 - Image size: 224 x 224 - **Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows: https://arxiv.org/abs/2103.14030 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Swin Transformer V2 image classification model. Pretrained on ImageNet-22k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 109.3 - GMACs: 11.9 - Activations (M): 39.7 - Image size: 192 x 192 - **Papers:** - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-22k Explore the dataset and runtime metrics of this model in timm model results.

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A Swin Transformer V2 image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 87.9 - GMACs: 22.0 - Activations (M): 84.7 - Image size: 256 x 256 - **Papers:** - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k Explore the dataset and runtime metrics of this model in timm model results.

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An independent implementation of Swin Transformer V2 released prior to the official code release. A collaboration between Christoph Reich and Ross Wightman, the model differs from official impl in a few ways: * MLP log relative position bias uses unnormalized natural log w/o scaling vs normalized, sigmoid clamped and scaled log2. * option to apply LayerNorm at end of every stage ("ns" variants). * defaults to NCHW tensor layout at output of each stage and final features. Pretrained on ImageNet-1k by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.3 - GMACs: 4.7 - Activations (M): 28.5 - Image size: 224 x 224 - **Papers:** - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Original:** https://github.com/ChristophReich1996/Swin-Transformer-V2 - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

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A Swin Transformer V2 image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 196.7 - GMACs: 47.8 - Activations (M): 121.5 - Image size: 256 x 256 - **Papers:** - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k Explore the dataset and runtime metrics of this model in timm model results.

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A Swin Transformer V2 image classification model. Pretrained on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.3 - GMACs: 6.7 - Activations (M): 39.0 - Image size: 256 x 256 - **Papers:** - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

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A Swin Transformer V2 image classification model. Pretrained on ImageNet-1k by paper authors. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.3 - GMACs: 6.0 - Activations (M): 24.6 - Image size: 256 x 256 - **Papers:** - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Original:** https://github.com/microsoft/Swin-Transformer - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

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