vivit b 16x2 kinetics400

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Categoryvideo-classification
Licensemit
Downloads448
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Overview

ViViT-B/16x2 is a Video Vision Transformer designed specifically for high-accuracy video classification. Unlike traditional CNN-based approaches that process frames sequentially, this model leverages a spatio-temporal attention mechanism to capture global dependencies across the video timeline. It is pre-trained on the Kinetics-400 dataset, making it a robust baseline for action recognition and activity detection tasks. For developers, this means a shift toward transformer-based architectures in video pipelines, offering better scalability and performance on long-range temporal patterns. It integrates well into PyTorch or TensorFlow workflows for developers building automated surveillance, sports analytics, or content tagging systems where understanding motion context is critical.

Highlights

  • Transformer-based architecture for global spatio-temporal feature extraction
  • Pre-trained on Kinetics-400 for diverse action recognition
  • Efficiently captures long-range temporal dependencies across frames
  • MIT licensed for flexible commercial and research integration

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("google/vivit-b-16x2-kinetics400")
tokenizer = AutoTokenizer.from_pretrained("google/vivit-b-16x2-kinetics400")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download google/vivit-b-16x2-kinetics400

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download google/vivit-b-16x2-kinetics400 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('google/vivit-b-16x2-kinetics400')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/google/vivit-b-16x2-kinetics400

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/google/vivit-b-16x2-kinetics400

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('google/vivit-b-16x2-kinetics400')
tokenizer = AutoTokenizer.from_pretrained('google/vivit-b-16x2-kinetics400')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model google/vivit-b-16x2-kinetics400

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model google/vivit-b-16x2-kinetics400 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('google/vivit-b-16x2-kinetics400')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/google/vivit-b-16x2-kinetics400.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/google/vivit-b-16x2-kinetics400.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'google/vivit-b-16x2-kinetics400')

Full Documentation

来源: HuggingFace

---
license: "mit"
tags:

  • vision

  • video-classification

---

ViViT (Video Vision Transformer)

ViViT model as introduced in the paper ViViT: A Video Vision Transformer by Arnab et al. and first released in this repository.

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) 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 to look for fine-tuned versions on a task that interests you.

How to use

For code examples, we refer to the documentation.

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}
}
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