|
--- |
|
license: "cc-by-nc-4.0" |
|
tags: |
|
- vision |
|
- video-classification |
|
--- |
|
|
|
# VideoMAE (base-sized model, pre-trained only) |
|
|
|
VideoMAE model pre-trained on Kinetics-400 for 1600 epochs in a self-supervised way. It was introduced in the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Tong et al. and first released in [this repository](https://github.com/MCG-NJU/VideoMAE). |
|
|
|
Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. |
|
|
|
## Model description |
|
|
|
VideoMAE is an extension of [Masked Autoencoders (MAE)](https://arxiv.org/abs/2111.06377) 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](https://huggingface.co/models?filter=videomae) to look for fine-tuned versions on a task that interests you. |
|
|
|
### How to use |
|
|
|
Here is how to use this model to predict pixel values for randomly masked patches: |
|
|
|
```python |
|
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") |
|
model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") |
|
|
|
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](https://huggingface.co/transformers/main/model_doc/videomae.html#). |
|
|
|
## Training data |
|
|
|
(to do, feel free to open a PR) |
|
|
|
## Training procedure |
|
|
|
### Preprocessing |
|
|
|
(to do, feel free to open a PR) |
|
|
|
### Pretraining |
|
|
|
(to do, feel free to open a PR) |
|
|
|
## Evaluation results |
|
|
|
(to do, feel free to open a PR) |
|
|
|
### BibTeX entry and citation info |
|
|
|
```bibtex |
|
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} |
|
} |
|
``` |
