For basic dataset information, you can refer to the dataset website.
Before we start, please make sure that the directory is located at $MMACTION/tools/data/mmit/
.
First of all, you can run the following script to prepare annotations.
bash download_data.sh
This part is optional if you only want to use the video loader.
Before extracting, please refer to install.md for installing dense_flow.
First, you can run the following script to soft link SSD.
# execute these two line (Assume the SSD is mounted at "/mnt/SSD/")
mkdir /mnt/SSD/mmit_extracted/
ln -s /mnt/SSD/mmit_extracted/ ../../../data/mmit/rawframes
If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract RGB-only frames.
bash extract_rgb_frames.sh
If both are required, run the following script to extract frames using "tvl1" algorithm.
bash extract_frames.sh
you can run the follow script to generate file list in the format of rawframes or videos.
bash generate_rawframes_filelist.sh
bash generate_videos_filelist.sh
After the whole data process for Multi-Moments in Time preparation, you will get the rawframes (RGB + Flow), videos and annotation files for Multi-Moments in Time.
In the context of the whole project (for Multi-Moments in Time only), the folder structure will look like:
mmaction/
└── data
└── mmit
├── annotations
│ ├── moments_categories.txt
│ ├── trainingSet.txt
│ └── validationSet.txt
├── mmit_train_rawframes.txt
├── mmit_train_videos.txt
├── mmit_val_rawframes.txt
├── mmit_val_videos.txt
├── rawframes
│ ├── 0-3-6-2-9-1-2-6-14603629126_5
│ │ ├── flow_x_00001.jpg
│ │ ├── flow_x_00002.jpg
│ │ ├── ...
│ │ ├── flow_y_00001.jpg
│ │ ├── flow_y_00002.jpg
│ │ ├── ...
│ │ ├── img_00001.jpg
│ │ └── img_00002.jpg
│ │ ├── ...
│ └── yt-zxQfALnTdfc_56
│ │ ├── ...
│ └── ...
└── videos
└── adult+female+singing
├── 0-3-6-2-9-1-2-6-14603629126_5.mp4
└── yt-zxQfALnTdfc_56.mp4
└── ...
For training and evaluating on Multi-Moments in Time, please refer to getting_started.md.