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VideoMAEv2 Huge

VideoMAEv2-Huge model pre-trained for 1200 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking]( by...

TipoOtherVersãoRevision b9125db

Fonte do modelo

Tipo
Other
Tamanho do arquivo
2,35 GB
Versão
vRevision b9125db
Atualizado
18 de set. de 2026
Downloads
3,6 mil
Fonte
Hugging Face

Descrição da fonte

VideoMAE-v2 (Huge-sized model, Pretrained on UnlabeledHybrid-1M)

VideoMAEv2-Huge model pre-trained for 1200 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking by Wang et al. and first released in GitHub.

Intended uses & limitations

You can use the raw model for video feature extraction.

How to use

Here is how to use this model to extract a video feature:

from transformers import VideoMAEImageProcessor, AutoModel, AutoConfig
import numpy as np
import torch


config = AutoConfig.from_pretrained("OpenGVLab/VideoMAEv2-Huge", trust_remote_code=True)
processor = VideoMAEImageProcessor.from_pretrained("OpenGVLab/VideoMAEv2-Huge")
model = AutoModel.from_pretrained('OpenGVLab/VideoMAEv2-Huge', config=config, trust_remote_code=True)


video = list(np.random.rand(16, 3, 224, 224))


# B, T, C, H, W -> B, C, T, H, W
inputs = processor(video, return_tensors="pt")
inputs['pixel_values'] = inputs['pixel_values'].permute(0, 2, 1, 3, 4)

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

BibTeX entry and citation info

@InProceedings{wang2023videomaev2,
    author    = {Wang, Limin and Huang, Bingkun and Zhao, Zhiyu and Tong, Zhan and He, Yinan and Wang, Yi and Wang, Yali and Qiao, Yu},
    title     = {VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {14549-14560}
}

@misc{videomaev2,
      title={VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking},
      author={Limin Wang and Bingkun Huang and Zhiyu Zhao and Zhan Tong and Yinan He and Yi Wang and Yali Wang and Yu Qiao},
      year={2023},
      eprint={2303.16727},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Fontes

1 fonte
Hugging FacePrimárioVersão b9125dbd0773cb9e9b2011e444085ea659397f53
huggingface.co/OpenGVLab/VideoMAEv2-Huge/tree/b9125dbd0773cb9e9b2011e444085ea659397f53

Verificado 18 de set.

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1 artefato

model.safetensors

safetensors · 2,35 GB · SHA-256 6a05040256a2…0338 · Hugging Face

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Trechos de fonte

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--- license: cc-by-nc-4.0 tags: - vision - video-classification pipeline_tag: video-classification --- # VideoMAE-v2 (Huge-sized model, Pretrained on UnlabeledHybrid-1M) VideoMAEv2-Huge model pre-trained for 1200 epochs in a self-supervised way on UnlabeldHybrid-1M dataset. It was introduced in the paper [[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://arxiv.org/abs/2203.12602) by Wang et al. and first released in [GitHub](https://github.com/OpenGVLab/VideoMAEv2). ## Intended uses & limitations You can use the raw model for video feature extraction. ### How to use Here is how to use this model to extract a video feature: ```python from transformers import VideoMAEImageProcessor, AutoModel, AutoConfig import numpy as np import torch config = AutoConfig.from_pretrained("OpenGVLab/VideoMAEv2-Huge", trust_remote_code=True) processor = VideoMAEImageProcessor.from_pretrained("OpenGVLab/VideoMAEv2-Huge") model = AutoModel.from_pretrained('OpenGVLab/VideoMAEv2-Huge', config=config, trust_remote_code=True) video = list(np.random.rand(16, 3, 224, 224)) # B, T, C, H, W -> B, C, T, H, W inputs = processor(video, return_tensors="pt") inputs['pixel_values'] = inputs['pixel_values'].permute(0, 2, 1, 3, 4) with torch.no_grad(): outputs = model(**inputs) ``` ### BibTeX entry and citation info ```bibtex @InProceedings{wang2023videomaev2, author = {Wang, Limin and Huang, Bingkun and Zhao, Zhiyu and Tong, Zhan and He, Yinan and Wang, Yi and Wang, Yali and Qiao, Yu}, title = {VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {14549-14560} } @misc{videomaev2, title={VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking}, author={Limin Wang and Bingkun Huang and Zhiyu Zhao and Zhan Tong and Yinan He and Yi Wang and Yali Wang and Yu Qiao}, year={2023}, eprint={2303.16727}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

Source context: 3578 downloads · 1 likes · Pipeline video-classification · Repo OpenGVLab/VideoMAEv2-Huge