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A ConvNeXt 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): 28.6 - GMACs: 4.5 - Activations (M): 13.4 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **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 ConvNeXt 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): 28.6 - GMACs: 13.1 - Activations (M): 39.5 - Image size: 384 x 384 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **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 ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 88.7 - GMACs: 15.4 - Activations (M): 28.8 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808 - **Original:** https://github.com/facebookresearch/ConvNeXt-V2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 198.0 - GMACs: 101.1 - Activations (M): 126.7 - Image size: 384 x 384 - **Papers:** - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808 - **Original:** https://github.com/facebookresearch/ConvNeXt-V2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.6 - GMACs: 2.5 - Activations (M): 8.4 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808 - **Original:** https://github.com/facebookresearch/ConvNeXt-V2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-1k. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 9.1 - GMACs: 1.4 - Activations (M): 6.1 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808 - **Original:** https://github.com/facebookresearch/ConvNeXt-V2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-22k and then ImageNet-1k. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.6 - GMACs: 4.5 - Activations (M): 13.4 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808 - **Original:** https://github.com/facebookresearch/ConvNeXt-V2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

Image classification
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