vivit b 16x2 kinetics400
简介
核心亮点
- 基于 Transformer 架构，擅长捕捉视频时空特征
- 在 Kinetics-400 数据集上经过验证，动作识别精准
- 适用于视频自动打标、短视频分类等工业场景
- MIT 协议开源，方便开发者快速集成与二次开发
使用方法
# 安装 Hugging Face transformers
pip install transformers torch# 使用 transformers 加载模型
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 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
pip install -U huggingface_hub命令行下载
下载完整模型库
huggingface-cli download google/vivit-b-16x2-kinetics400下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download google/vivit-b-16x2-kinetics400 config.json --local-dir ./dirSDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('google/vivit-b-16x2-kinetics400')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://huggingface.co/google/vivit-b-16x2-kinetics400如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/google/vivit-b-16x2-kinetics400模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
pip install -U transformers torch模型加载和推理
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('google/vivit-b-16x2-kinetics400')
tokenizer = AutoTokenizer.from_pretrained('google/vivit-b-16x2-kinetics400')模型下载
我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 ModelScope：
pip install modelscope命令行下载
下载完整模型库
modelscope download --model google/vivit-b-16x2-kinetics400下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）
modelscope download --model google/vivit-b-16x2-kinetics400 README.md --local_dir ./dirSDK 下载
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('google/vivit-b-16x2-kinetics400')Git 下载
请确保 lfs 已经被正确安装
git lfs install
git clone https://www.modelscope.cn/google/vivit-b-16x2-kinetics400.git如果您希望跳过 lfs 大文件下载，可以使用如下命令
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/google/vivit-b-16x2-kinetics400.gitModelScope 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 快速开发
下载并安装 ModelScope library
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html模型加载和推理
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
p = pipeline('text-generation', 'google/vivit-b-16x2-kinetics400')完整文档
---
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
@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}
}