Videomae Base Short Ssv2

🚀 VideoMAE (base-sized model, pre-trained only)

A VideoMAE model pre-trained on Something-Something-v2 for 800 epochs in a self-supervised way, offering data-efficient learning for video pre-training.

✨ Features

• VideoMAE extends Masked Autoencoders (MAE) to video, with an architecture similar to a standard Vision Transformer (ViT) and a decoder for predicting pixel values of masked patches.
• It learns an inner representation of videos during pre - training, which can be used to extract features for downstream tasks.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

Here is how to use this model to predict pixel values for randomly masked patches:

from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining
import numpy as np
import torch

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

processor = VideoMAEImageProcessor.from_pretrained("MCG-NJU/videomae-base-short-ssv2")
model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short-ssv2")

pixel_values = processor(video, return_tensors="pt").pixel_values

num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2
seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame
bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()

outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
loss = outputs.loss

For more code examples, we refer to the documentation.

📚 Documentation

Model description

VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches.

Videos are presented to the model as a sequence of fixed - size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder.

By pre - training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre - trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.

Intended uses & limitations

You can use the raw model for predicting pixel values for masked patches of a video, but it's mostly intended to be fine - tuned on a downstream task. See the model hub to look for fine - tuned versions on a task that interests you.

🔧 Technical Details

The model pre - trains on Something - Something - v2 for 800 epochs in a self - supervised way. It uses a sequence of fixed - size patches from videos, adds [CLS] tokens for classification, and fixed position embeddings before the Transformer encoder layers. The pre - trained model can learn an inner representation of videos for downstream tasks.

📄 License

This model is released under the "cc - by - nc - 4.0" license.

BibTeX entry and citation info

misc{https://doi.org/10.48550/arxiv.2203.12602,
  doi = {10.48550/ARXIV.2203.12602},
  url = {https://arxiv.org/abs/2203.12602},
  author = {Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}