---
title: vivit-b-16x2-kinetics400
canonical_url: "https://www.modelscope.cn/models/google/vivit-b-16x2-kinetics400"
md_url: "https://www.modelscope.cn/models/google/vivit-b-16x2-kinetics400.md"
repository: google/vivit-b-16x2-kinetics400
last_updated: 2025-04-21
license: mit
model_type:
  - vivit
architectures:
  - ViViTForVideoClassification
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 448
stars: 0
tags:
  - vision
  - video-classification
---

# vivit-b-16x2-kinetics400

> vivit-b-16x2-kinetics400 - google 在 ModelScope 开源的模型。ViViT (Video Vision Transformer)

google/vivit-b-16x2-kinetics400 是 ModelScope 魔搭社区上的机器学习模型，采用 mit 许可。

- **Repository**: google/vivit-b-16x2-kinetics400
- **License**: mit
- **Tags**: vision, video-classification
- **Downloads**: 448
- **Stars**: 0
- **Last updated**: 2025-04-21

Source: https://www.modelscope.cn/models/google/vivit-b-16x2-kinetics400

---

# ViViT (Video Vision Transformer) 

ViViT model as introduced in the paper [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Arnab et al. and first released in [this repository](https://github.com/google-research/scenic/tree/main/scenic/projects/vivit). 

Disclaimer: The team releasing ViViT did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

ViViT is an extension of the [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/v4.27.0/model_doc/vit) to video.

We refer to the paper for details.

## Intended uses & limitations

The model is mostly meant to intended to be fine-tuned on a downstream task, like video classification. See the [model hub](https://huggingface.co/models?filter=vivit) to look for fine-tuned versions on a task that interests you.

### How to use

For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/vivit).

### BibTeX entry and citation info

```bibtex
@misc{arnab2021vivit,
      title={ViViT: A Video Vision Transformer}, 
      author={Anurag Arnab and Mostafa Dehghani and Georg Heigold and Chen Sun and Mario Lučić and Cordelia Schmid},
      year={2021},
      eprint={2103.15691},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```