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Preparing Diving48

Introduction

@inproceedings{li2018resound,
  title={Resound: Towards action recognition without representation bias},
  author={Li, Yingwei and Li, Yi and Vasconcelos, Nuno},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={513--528},
  year={2018}
}

For basic dataset information, you can refer to the official dataset website.

MIM supports downloading from OpenDataLab and preprocessing Diving48 dataset with one command line.

# install OpenXlab CLI tools
pip install -U openxlab
# log in OpenXLab
openxlab login
# download and preprocess by MIM
mim download mmaction2 --dataset diving48

Check Directory Structure

After the whole data process for Diving48 preparation, you will get the rawframes (RGB + Flow), videos and annotation files for Diving48.

In the context of the whole project (for Diving48 only), the folder structure will look like:

mmaction2
├── mmaction
├── tools
├── configs
├── data
│   ├── diving48
│   │   ├── diving48_{train,val}_list_rawframes.txt
│   │   ├── diving48_{train,val}_list_videos.txt
│   │   ├── annotations (optinonal)
│   |   |   ├── Diving48_V2_train.json
│   |   |   ├── Diving48_V2_test.json
│   |   |   ├── Diving48_vocab.json
│   |   ├── videos
│   |   |   ├── _8Vy3dlHg2w_00000.mp4
│   |   |   ├── _8Vy3dlHg2w_00001.mp4
│   |   |   ├── ...
│   |   ├── rawframes (optional)
│   |   |   ├── 2x00lRzlTVQ_00000
│   |   |   |   ├── img_00001.jpg
│   |   |   |   ├── img_00002.jpg
│   |   |   |   ├── ...
│   |   |   |   ├── flow_x_00001.jpg
│   |   |   |   ├── flow_x_00002.jpg
│   |   |   |   ├── ...
│   |   |   |   ├── flow_y_00001.jpg
│   |   |   |   ├── flow_y_00002.jpg
│   |   |   |   ├── ...
│   |   |   ├── 2x00lRzlTVQ_00001
│   |   |   ├── ...

For training and evaluating on Diving48, please refer to Training and Test Tutorial.