license: apache-2.0 tags:

• 视觉
• 图像分类 datasets:
• imagenet-1k widget:
• src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: 老虎
• src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: 茶壶
• src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: 宫殿

ConvNeXT (微型模型)

ConvNeXT模型在ImageNet-1k数据集上以224x224分辨率训练而成。该模型由Liu等人在论文《A ConvNet for the 2020s》中提出，并首次发布于此代码库。

免责声明：发布ConvNeXT的团队未为此模型编写模型卡，因此本模型卡由Hugging Face团队撰写。

模型描述

ConvNeXT是一个纯卷积模型（ConvNet），受视觉Transformer设计的启发，声称性能超越Transformer。作者从ResNet出发，以Swin Transformer为灵感，“现代化”了其设计。

预期用途与限制

您可以将原始模型用于图像分类任务。访问模型中心寻找针对您感兴趣任务的微调版本。

使用方法

以下示例展示如何使用该模型将COCO 2017数据集中的图像分类为ImageNet的1,000个类别之一：

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-tiny-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")

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

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

# 模型预测ImageNet千分类别之一
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),

更多代码示例请参阅文档。

BibTeX引用信息

@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}
}