Videomaev2 Huge

🚀 VideoMAE-v2（超大模型，在UnlabeledHybrid-1M上預訓練）

VideoMAE-v2 超大模型在 UnlabeledHybrid-1M 數據集上以自監督方式預訓練了 1200 個輪次。該模型由 Wang 等人在論文 [CVPR23]VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking 中提出，並首次在 GitHub 上發佈。

✨ 主要特性

• 可用於視頻特徵提取。

🚀 快速開始

預期用途和限制

你可以使用該原始模型進行視頻特徵提取。

使用方法

以下是如何使用此模型提取視頻特徵的示例：

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)

📚 詳細文檔

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}
}

📄 許可證

本項目採用 CC BY-NC 4.0 許可證。