facebook / convnext-base-224-22k

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Model's Last Updated: Junio 13 2023
image-classification

Introduction of convnext-base-224-22k

Model Details of convnext-base-224-22k

ConvNeXT (base-sized model)

ConvNeXT model trained on ImageNet-22k 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:

from transformers import ConvNextImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset

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

processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-base-224-22k")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-base-224-22k")

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

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

# model predicts one of the 22k ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),

For more code examples, we refer to the documentation .

BibTeX entry and citation info
@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}
}

Runs of facebook convnext-base-224-22k on huggingface.co

2.9K
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More Information About convnext-base-224-22k huggingface.co Model

More convnext-base-224-22k license Visit here:

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convnext-base-224-22k huggingface.co

convnext-base-224-22k huggingface.co is an AI model on huggingface.co that provides convnext-base-224-22k's model effect (), which can be used instantly with this facebook convnext-base-224-22k model. huggingface.co supports a free trial of the convnext-base-224-22k model, and also provides paid use of the convnext-base-224-22k. Support call convnext-base-224-22k model through api, including Node.js, Python, http.

convnext-base-224-22k huggingface.co Url

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facebook convnext-base-224-22k online free

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facebook convnext-base-224-22k online free url in huggingface.co:

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convnext-base-224-22k install

convnext-base-224-22k is an open source model from GitHub that offers a free installation service, and any user can find convnext-base-224-22k on GitHub to install. At the same time, huggingface.co provides the effect of convnext-base-224-22k install, users can directly use convnext-base-224-22k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

convnext-base-224-22k install url in huggingface.co:

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