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- **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 99.3 - GMACs: 15.1 - Activations (M): 35.1 - Image size: 224 x 224 - **Papers:** - Twins: Revisiting the Design of Spatial Attention in Vision Transformers: https://arxiv.org/abs/2104.13840 - **Dataset:** ImageNet-1k - **Original:** https://github.com/Meituan-AutoML/Twins Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A VGG image classification model. Trained on ImageNet-1k, original torchvision weights. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 138.4 - GMACs: 15.5 - Activations (M): 13.6 - Image size: 224 x 224 - **Papers:** - Very Deep Convolutional Networks for Large-Scale Image Recognition: https://arxiv.org/abs/1409.1556 - **Dataset:** ImageNet-1k - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A VGG image classification model. Trained on ImageNet-1k, original torchvision weights. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 143.7 - GMACs: 19.6 - Activations (M): 14.9 - Image size: 224 x 224 - **Papers:** - Very Deep Convolutional Networks for Large-Scale Image Recognition: https://arxiv.org/abs/1409.1556 - **Dataset:** ImageNet-1k - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Visformer image classification model. Trained on ImageNet-1k by https://github.com/hzhang57 and https://github.com/developer0hye. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 40.2 - GMACs: 4.9 - Activations (M): 11.4 - Image size: 224 x 224 - **Papers:** - Visformer: The Vision-friendly Transformer: https://arxiv.org/abs/2104.12533 - **Dataset:** ImageNet-1k - **Original:** https://github.com/danczs/Visformer Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 102.6 - GMACs: 16.9 - Activations (M): 16.5 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.6 - GMACs: 16.9 - Activations (M): 16.5 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k by paper authors and (re) fine-tuned on ImageNet-1k with additional augmentation and regularization by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.6 - GMACs: 16.9 - Activations (M): 16.5 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.9 - GMACs: 49.4 - Activations (M): 48.3 - Image size: 384 x 384 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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