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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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# VideoMAE (base-sized model, fine-tuned on Kinetics-400) |
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VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. 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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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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## Model description |
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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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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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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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## Intended uses & limitations |
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You can use the raw model for video classification into one of the 400 possible Kinetics-400 labels. |
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### How to use |
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Here is how to use this model to classify a video: |
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```python |
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from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification |
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import numpy as np |
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import torch |
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video = list(np.random.randn(16, 3, 224, 224)) |
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processor = VideoMAEImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") |
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model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") |
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inputs = processor(video, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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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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## Training data |
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(to do, feel free to open a PR) |
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## Training procedure |
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### Preprocessing |
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(to do, feel free to open a PR) |
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### Pretraining |
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(to do, feel free to open a PR) |
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## Evaluation results |
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This model obtains a top-1 accuracy of 80.9 and a top-5 accuracy of 94.7 on the test set of Kinetics-400. |
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### BibTeX entry and citation info |
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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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``` |
