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Dataset Preparation

Kinetics

The Kinetics Dataset could be downloaded via the code released by ActivityNet:

  1. Download the videos via the official scripts.

  2. After all the videos were downloaded, resize the video to the short edge size of 256, then prepare the csv files for training, validation, and testing set as train.csv, val.csv, test.csv. The format of the csv file is:

path_to_video_1 label_1
path_to_video_2 label_2
path_to_video_3 label_3
...
path_to_video_N label_N

You can use provided helper functions to create csv files:

cd data/kinetics_400
python3 preprocess.py --root_dir $PATH_TO_ROOT_DIR --split_dir $SPLIT_DIR --mode $MODE

For example:

cd data/kinetics_400
python3 preprocess.py --root_dir /datasets01/kinetics/070618/400/ --split_dir train_avi-288p --mode train
python3 preprocess.py --root_dir /datasets01/kinetics/070618/400/ --split_dir val_avi-288p --mode val
python3 preprocess.py --root_dir /datasets01/kinetics/070618/400/ --split_dir val_avi-288p --mode test

Something-Something V2

  1. Please download the dataset and annotations from dataset provider.

  2. Download the frame list from the following links: (train, val).

  3. Extract the frames at 30 FPS. (We used ffmpeg-4.1.3 with command ffmpeg -i "${video}" -r 30 -q:v 1 "${out_name}" in experiments.) Please put the frames in a structure consistent with the frame lists.

Please put all annotation json files and the frame lists in the same folder, and set DATA.PATH_TO_DATA_DIR to the path. Set DATA.PATH_PREFIX to be the path to the folder containing extracted frames.

Epic-Kitchens-100

Follow instructions from dataset provider.