Tiny Kinetics-400 for test

Overview

Kinetics-400迷你数据集

English | 简体中文

该数据集旨在解决的问题:参照Kinetics-400数据格式,训练基于自己数据的视频理解模型。

数据集介绍

Kinetics-400是视频领域benchmark常用数据集,详细介绍可以参考其官方网站Kinetics。整个数据集包含400个类别,全部文件大概需要135G左右的存储空间,下载起来比较困难。

Tiny-Kinetics-400同样包含400个类别,每个类别下仅有两条视频数据,分为train与val,可用于调试一些视频理解模型。

具体对比如下:

数据集 训练条数 验证条数 大小
Kinetics-400 234619 19761 135G
Tiny-Kinetics-400 400 400 420M

Tiny-Kinetics-400下载

目前提供了百度网盘的下载方式:

下载方式 链接
百度云 BaiduCloud (1cns)

抽帧Extract Frames

通常在训练视频理解模型时,会提前对视频文件进行抽帧,以此来加速训练过程。这里提供了抽帧脚本,且满足以下条件:

  • 每个视频只抽取300帧
  • 如果整个视频多于300帧,直接舍弃之后的视频帧
  • 如果整个视频少于300帧,复制最后的视频帧以填充至300帧

使用方式:

python ./tools/extract_frames.py --source_dir ~/data/tiny-kinetics-400/train_256 ~/data/kinetics400_30fps_frames/train
python ./tools/extract_frames.py --source_dir ~/data/tiny-kinetics-400/val_256 ~/data/kinetics400_30fps_frames/val

将meta文件移到视频帧目录下:

mv ./annotations/tiny_train.csv ~/data/kinetics400_30fps_frames/
mv ./annotations/tiny_val.csv ~/data/kinetics400_30fps_frames/

最终的目录结构如下:

kinetics400_30fps_frames/
├── train/
│   ├── abseiling/
│   │   ├──_4YTwq0-73Y_000044_000054
│   │   │  ├──frame_00001.jpg
│   │   │  ├──...
│   │   ├──...
│   ├──...
├── val/
│   ├── abseiling/
│   │   ├──-3B32lodo2M_000059_000069
│   │   │  ├──frame_00001.jpg
│   │   │  ├──...
│   │   ├──...
│   ├──...
├── tiny_train.csv
├── tiny_val.csv

TODO

  • 更多下载方式

参考

Owner
Data&Model&Loss
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