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+ ---
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+ license: "cc-by-nc-4.0"
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+ tags:
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+ - vision
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+ - video-classification
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+ ---
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+
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+ # VideoMAE (base-sized model, pre-trained only)
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+
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+ 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).
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+
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+ 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.
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+
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+ ## Model description
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+
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+ 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.
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+
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+ 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.
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+
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+ 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.
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+
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+ ## Intended uses & limitations
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+
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+ 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.
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+
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+ ### How to use
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+
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+ Here is how to use this model to predict pixel values for randomly masked patches:
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+
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+ ```python
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+ from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining
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+ import numpy as np
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+ import torch
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+
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+ num_frames = 16
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+ video = list(np.random.randn(16, 3, 224, 224))
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+
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+ processor = VideoMAEImageProcessor.from_pretrained("MCG-NJU/videomae-base")
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+ model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base")
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+
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+ pixel_values = processor(video, return_tensors="pt").pixel_values
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+
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+ num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2
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+ seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame
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+ bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()
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+
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+ outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
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+ loss = outputs.loss
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+ ```
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+
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+ For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/videomae.html#).
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+
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+ ## Training data
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+
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+ (to do, feel free to open a PR)
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+
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+ ## Training procedure
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+
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+ ### Preprocessing
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+
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+ (to do, feel free to open a PR)
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+
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+ ### Pretraining
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+
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+ (to do, feel free to open a PR)
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+
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+ ## Evaluation results
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+
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+ (to do, feel free to open a PR)
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+
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+ ### BibTeX entry and citation info
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+
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+ ```bibtex
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+ misc{https://doi.org/10.48550/arxiv.2203.12602,
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+ doi = {10.48550/ARXIV.2203.12602},
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+ url = {https://arxiv.org/abs/2203.12602},
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+ author = {Tong, Zhan and Song, Yibing and Wang, Jue and Wang, Limin},
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+ keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+ title = {VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training},
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+ publisher = {arXiv},
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+ year = {2022},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```