Create README.md

README.md

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+---
+license: "cc-by-nc-4.0"
+tags:
+- vision
+- video-classification
+---
+
+# VideoMAE (base-sized model, fine-tuned on Kinetics-400) 
+
+VideoMAE model fine-tuned on Kinetics-400 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 video classification into one of the 400 possible Kinetics-400 labels.
+
+### How to use
+
+Here is how to use this model to classify a video:
+
+```python
+from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
+import numpy as np
+import torch
+
+video = list(np.random.randn(16, 3, 224, 224))
+
+feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
+model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base")
+
+inputs = feature_extractor(video, return_tensors="pt")
+
+with torch.no_grad():
+  outputs = model(**inputs)
+  logits = outputs.logits
+
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
+
+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}
+}
+```