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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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# TimeSformer (base-sized model, fine-tuned on Kinetics-600) |
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TimeSformer model pre-trained on [Kinetics-600](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). |
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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). |
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## Intended uses & limitations |
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You can use the raw model for video classification into one of the 600 possible Kinetics-600 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 AutoImageProcessor, TimesformerForVideoClassification |
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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, 448, 448)) |
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processor = AutoImageProcessor.from_pretrained("facebook/timesformer-hr-finetuned-k600") |
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model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-k600") |
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inputs = processor(images=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/timesformer.html#). |
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### BibTeX entry and citation info |
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```bibtex |
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@inproceedings{bertasius2021space, |
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title={Is Space-Time Attention All You Need for Video Understanding?}, |
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author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo}, |
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booktitle={International Conference on Machine Learning}, |
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pages={813--824}, |
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year={2021}, |
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organization={PMLR} |
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} |
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``` |
