add model card (#1)

- add model card (6e4c16d3ce1a75d81d5c93582e702f0f15c8a6a8)


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

README.md

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+---
+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 TimesformerFeatureExtractor, TimesformerForVideoClassification
+import numpy as np
+import torch
+
+video = list(np.random.randn(8, 3, 224, 224))
+
+feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-base-finetuned-k600")
+model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k600")
+
+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}
+}
+```