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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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datasets: |
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- imagenet-22k |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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--- |
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# ConvNeXt V2 (base-sized model) |
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ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 224x224. It was introduced in the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Woo et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt-V2). |
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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. |
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## Model description |
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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 recognition benchmarks. |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnextv2) to look for |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
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```python |
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from transformers import AutoImageProcessor, ConvNextV2ForImageClassification |
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import torch |
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from datasets import load_dataset |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-base-22k-224") |
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model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-base-22k-224") |
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inputs = preprocessor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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# model predicts one of the 1000 ImageNet classes |
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predicted_label = logits.argmax(-1).item() |
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print(model.config.id2label[predicted_label]), |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnextv2). |
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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2301-00808, |
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author = {Sanghyun Woo and |
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Shoubhik Debnath and |
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Ronghang Hu and |
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Xinlei Chen and |
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Zhuang Liu and |
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In So Kweon and |
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Saining Xie}, |
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title = {ConvNeXt {V2:} Co-designing and Scaling ConvNets with Masked Autoencoders}, |
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journal = {CoRR}, |
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volume = {abs/2301.00808}, |
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year = {2023}, |
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url = {https://doi.org/10.48550/arXiv.2301.00808}, |
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doi = {10.48550/arXiv.2301.00808}, |
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eprinttype = {arXiv}, |
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eprint = {2301.00808}, |
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timestamp = {Tue, 10 Jan 2023 15:10:12 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2301-00808.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
