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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...

TipoOtherVersiónRevision b9125db

Fuente del modelo

Tipo
Other
Tamaño del archivo
2,35 GB
Versión
vRevision b9125db
Actualizado
18 sept 2026
Descargas
3,6 mil
Fuente
Hugging Face

Descripción de la fuente

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}
}

Fuentes

1 fuente
Hugging FacePrincipalVersión b9125dbd0773cb9e9b2011e444085ea659397f53
huggingface.co/OpenGVLab/VideoMAEv2-Huge/tree/b9125dbd0773cb9e9b2011e444085ea659397f53

Verificado 18 sept

Artefactos del modelo

1 artefacto

model.safetensors

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

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Extractos de fuentes

2 extractos

--- 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