readme: ---
  license: "cc-by-nc-4.0"
  tags:
  - vision
  - video-classification
  ---
  
  # TimeSformer (base-sized model, fine-tuned on Kinetics-600) 
  
  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). 
  
  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 600 possible Kinetics-600 labels.
  
  ### How to use
  
  Here is how to use this model to classify a video:
  
  ```python
  from transformers import AutoImageProcessor, TimesformerForVideoClassification
  import numpy as np
  import torch
  
  video = list(np.random.randn(8, 3, 224, 224))
  
  processor = AutoImageProcessor.from_pretrained("fcakyon/timesformer-base-finetuned-k600")
  model = TimesformerForVideoClassification.from_pretrained("fcakyon/timesformer-base-finetuned-k600")
  
  inputs = processor(images=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}
  }
  ```