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add model card (#1)

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- add model card (6e4c16d3ce1a75d81d5c93582e702f0f15c8a6a8)


Co-authored-by: Fatih <[email protected]>

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  1. README.md +55 -0
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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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+
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+ # TimeSformer (base-sized model, fine-tuned on Kinetics-600)
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+
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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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+
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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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+
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+ ## Intended uses & limitations
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+
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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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+
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+ ### How to use
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+
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+ Here is how to use this model to classify a video:
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+
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+ ```python
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+ from transformers import TimesformerFeatureExtractor, TimesformerForVideoClassification
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+ import numpy as np
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+ import torch
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+
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+ video = list(np.random.randn(8, 3, 224, 224))
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+
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+ feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-base-finetuned-k600")
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+ model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k600")
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+
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+ inputs = feature_extractor(video, return_tensors="pt")
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+
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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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+
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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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+
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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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+
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+ ### BibTeX entry and citation info
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+
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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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+ ```