Timesformer Base Finetuned Ssv2

🚀 TimeSformer (base-sized model, fine-tuned on Something Something v2)

TimeSformer is a pre-trained model on Something Something v2, which can be used for video classification tasks.

🚀 Quick Start

TimeSformer model is pre-trained on Something Something v2. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository.

Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by fcakyon.

✨ Features

You can use the raw model for video classification into one of the 174 possible Something Something v2 labels.

💻 Usage Examples

Basic Usage

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-ssv2")
model = TimesformerForVideoClassification.from_pretrained("fcakyon/timesformer-base-finetuned-ssv2")

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.

📚 Documentation

BibTeX entry and citation info

@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}
}

📄 License

The license of this project is cc-by-nc-4.0.