
# ConvNeXT (xlarge-sized model) 

ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). 

Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.png)

## Intended uses & limitations

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

```python
from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-xlarge-224-22k-1k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-xlarge-224-22k-1k")

inputs = feature_extractor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```

For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-2201-03545,
  author    = {Zhuang Liu and
               Hanzi Mao and
               Chao{-}Yuan Wu and
               Christoph Feichtenhofer and
               Trevor Darrell and
               Saining Xie},
  title     = {A ConvNet for the 2020s},
  journal   = {CoRR},
  volume    = {abs/2201.03545},
  year      = {2022},
  url       = {https://arxiv.org/abs/2201.03545},
  eprinttype = {arXiv},
  eprint    = {2201.03545},
  timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-21k", "imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
transformers pytorch tf convnext image-classification vision dataset:imagenet-21k dataset:imagenet-1k arxiv:2201.03545 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# ConvNeXT (xlarge-sized model) 

ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. 

Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.

!model image

## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:



For more code examples, we refer to the documentation.

### BibTeX entry and citation info


# ConvNeXT (xlarge-sized model) 

ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. 

Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description

ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.

!model image ## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you. ### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:



For more code examples, we refer to the documentation. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # ConvNeXT (xlarge-sized model) 

ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. 

Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description

ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.

!model image ## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you. ### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:



For more code examples, we refer to the documentation. ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #tf #convnext #image-classification #vision #dataset-imagenet-21k #dataset-imagenet-1k #arxiv-2201.03545 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# ConvNeXT (xlarge-sized model) 

ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. 

Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description

ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.

!model image## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:



For more code examples, we refer to the documentation.### BibTeX entry and citation info