convnextv2_base.fcmae_ft_in22k_in1k

Model · Image classification

convnextv2-base-22k-384

AI at Meta

ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 384x384. It was introduced in the paper ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Woo et al. and first released in this repository. Disclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team. ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various…

Model · Image classification

convnext_base.fb_in22k_ft_in1k

PyTorch Image Models

A ConvNeXt image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.

Model · Image classification

convnext_base.clip_laion2b_augreg_ft_in12k_in1k

PyTorch Image Models

A ConvNeXt image classification model. CLIP image tower weights pretrained in OpenCLIP on LAION and fine-tuned on ImageNet-12k followed by ImageNet-1k in timm bby Ross Wightman. Please see related OpenCLIP model cards for more details on pretrain: https://huggingface.co/laion/CLIP-convnextxxlarge-laion2B-s34B-b82K-augreg-soup https://huggingface.co/laion/CLIP-convnextlarged.laion2B-s26B-b102K-augreg https://huggingface.co/laion/CLIP-convnextbasew-laion2B-s13B-b82K-augreg https://huggingface.co/laion/CLIP-convnextbasew320-laionaesthetic-s13B-b82K - Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020 Explore the dataset and runtime metrics…

Model · Image classification

vit_base_patch16_384.augreg_in21k_ft_in1k

PyTorch Image Models

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. - 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 Explore the dataset and runtime metrics of this model in timm model results.

Model · Image classification

swin_base_patch4_window12_384.ms_in22k_ft_in1k

PyTorch Image Models

A Swin Transformer image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors. Explore the dataset and runtime metrics of this model in timm model results.

Model · Image classification

vit_base_patch8_224.augreg2_in21k_ft_in1k

PyTorch Image Models

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. - 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 Explore the dataset and runtime metrics of this model in timm model results.