700]¶

Introduction¶

@inproceedings{inproceedings,
  author = {Carreira, J. and Zisserman, Andrew},
  year = {2017},
  month = {07},
  pages = {4724-4733},
  title = {Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset},
  doi = {10.1109/CVPR.2017.502}
}

For basic dataset information, please refer to the official website.

Note

Because of the expirations of some YouTube links, the sizes of kinetics dataset copies may be different. Here are the sizes of our kinetics dataset copies that used to train all checkpoints.

Dataset	training videos	validation videos
kinetics400	240436	19796
Kinetics600	383393	27910
Kinetics700	542357	34824

Note

All experiments on Kinetics in MMAction2 are based on this version, we recommend users to try this version.

MIM supports downloading from OpenDataLab and preprocessing Kinetics-400/600/700 dataset with one command line.

# install OpenXlab CLI tools
pip install -U openxlab
# log in OpenXLab
openxlab login
# download and preprocess Kinetics-400 by MIM. Note that this might take a long time.
mim download mmaction2 --dataset kinetics400
# download and preprocess Kinetics-600 by MIM. Note that this might take a long time.
mim download mmaction2 --dataset kinetics600
# download and preprocess Kinetics-700 by MIM. Note that this might take a long time.
mim download mmaction2 --dataset kinetics700

Step 1. Prepare Annotations

The scripts can be used for preparing kinetics400, kinetics600, kinetics700. To prepare different version of kinetics, you need to replace ${DATASET} in the following examples with the specific dataset name. The choices of dataset names are kinetics400, kinetics600 and kinetics700. Before we start, please make sure that the directory is located at $MMACTION2/tools/data/${DATASET}/.

First of all, you can run the following script to prepare annotations by downloading from the official website.

bash download_annotations.sh ${DATASET}

Since some video urls are invalid, the number of video items in current official annotations are less than the original official ones. So we provide an alternative way to download the older one as a reference. Among these, the annotation files of Kinetics400 and Kinetics600 are from official crawler, the annotation files of Kinetics700 are from website downloaded in 05/02/2021.

bash download_backup_annotations.sh ${DATASET}

Step 2. Prepare Videos

you can run the following script to prepare videos. The codes are adapted from the official crawler. Note that this might take a long time.

bash download_videos.sh ${DATASET}

Important: If you have already downloaded video dataset using the download script above, you must replace all whitespaces in the class name for ease of processing by running

bash rename_classnames.sh ${DATASET}

For better decoding speed, you can resize the original videos into smaller sized, densely encoded version by:

python ../resize_videos.py ../../../data/${DATASET}/videos_train/ ../../../data/${DATASET}/videos_train_256p_dense_cache --dense --level 2

You can also download from Academic Torrents (kinetics400 & kinetics700 with short edge 256 pixels are available) and cvdfoundation/kinetics-dataset (Host by Common Visual Data Foundation and Kinetics400/Kinetics600/Kinetics-700-2020 are available)

Step 3. Extract RGB and Flow

This part is optional if you only want to use the video loader.

Before extracting, please refer to install.md for installing denseflow.

If you have plenty of SSD space, then we recommend extracting frames there for better I/O performance. And you can run the following script to soft link the extracted frames.

# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
mkdir /mnt/SSD/${DATASET}_extracted_train/
ln -s /mnt/SSD/${DATASET}_extracted_train/ ../../../data/${DATASET}/rawframes_train/
mkdir /mnt/SSD/${DATASET}_extracted_val/
ln -s /mnt/SSD/${DATASET}_extracted_val/ ../../../data/${DATASET}/rawframes_val/

If you only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract RGB-only frames using denseflow.

bash extract_rgb_frames.sh ${DATASET}

If you didn’t install denseflow, you can still extract RGB frames using OpenCV by the following script, but it will keep the original size of the images.

bash extract_rgb_frames_opencv.sh ${DATASET}

If both are required, run the following script to extract frames.

bash extract_frames.sh ${DATASET}

The commands above can generate images with new short edge 256. If you want to generate images with short edge 320 (320p), or with fix size 340x256, you can change the args --new-short 256 to --new-short 320 or --new-width 340 --new-height 256. More details can be found in prepare dataset.

Step 4. Generate File List

you can run the follow scripts to generate file list in the format of videos and rawframes, respectively.

bash generate_videos_filelist.sh ${DATASET}
# execute the command below when rawframes are ready
bash generate_rawframes_filelist.sh ${DATASET}

Folder Structure¶

After the whole data pipeline for Kinetics preparation. you can get the rawframes (RGB + Flow), videos and annotation files for Kinetics.

In the context of the whole project (for Kinetics only), the minimal folder structure will look like: (minimal means that some data are not necessary: for example, you may want to evaluate kinetics using the original video format.)

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── ${DATASET}
│   │   ├── ${DATASET}_train_list_videos.txt
│   │   ├── ${DATASET}_val_list_videos.txt
│   │   ├── annotations
│   │   ├── videos_train
│   │   ├── videos_val
│   │   │   ├── abseiling
│   │   │   │   ├── 0wR5jVB-WPk_000417_000427.mp4
│   │   │   │   ├── ...
│   │   │   ├── ...
│   │   │   ├── wrapping_present
│   │   │   ├── ...
│   │   │   ├── zumba
│   │   ├── rawframes_train (optional)
│   │   ├── rawframes_val (optional)

For training and evaluating on Kinetics, please refer to Training and Test Tutorial.