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---
license: "cc-by-nc-4.0"
tags:
- vision
- video-classification
---
# TimeSformer (base-sized model, fine-tuned on Kinetics-400)
TimeSformer model pre-trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [TimeSformer: Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Tong et al. and first released in [this repository](https://github.com/facebookresearch/TimeSformer).
Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by [fcakyon](https://github.com/fcakyon).
## 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, TimesformerForVideoClassification
import numpy as np
import torch
video = list(np.random.randn(8, 3, 224, 224))
feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400")
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/timesformer.html#).
### BibTeX entry and citation info
```bibtex
@inproceedings{bertasius2021space,
title={Is Space-Time Attention All You Need for Video Understanding?},
author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
booktitle={International Conference on Machine Learning},
pages={813--824},
year={2021},
organization={PMLR}
}
``` |