🚀 VideoMAE-v2 (巨型模型,在UnlabeledHybrid-1M上预训练)
VideoMAE-v2是一个巨型模型,在UnlabeldHybrid-1M数据集上以自监督的方式进行了1200个轮次的预训练。该模型由Wang等人在论文[CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking中提出,并首次在GitHub上发布。
🚀 快速开始
VideoMAE-v2模型可用于视频特征提取。以下是使用该模型提取视频特征的示例代码:
from transformers import VideoMAEImageProcessor, AutoModel, AutoConfig
import numpy as np
import torch
config = AutoConfig.from_pretrained("OpenGVLab/VideoMAEv2-giant", trust_remote_code=True)
processor = VideoMAEImageProcessor.from_pretrained("OpenGVLab/VideoMAEv2-giant")
model = AutoModel.from_pretrained('OpenGVLab/VideoMAEv2-giant', config=config, trust_remote_code=True)
video = list(np.random.rand(16, 3, 224, 224))
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)
✨ 主要特性
📚 详细文档
预期用途和局限性
你可以使用原始模型进行视频特征提取。
BibTeX引用和引用信息
@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}
}
📄 许可证
本项目采用CC BY-NC 4.0许可证。