Preparation of AVA-Dataset Format for Training on Custom Dataset

Yes! Let’s say your dataset is named MyData, you should structure your files and folders like this:

SlowFast/
├── configs/
│       └── MyData/
│               └── I3D_8x8_R50.yaml
├── data/
│       └── MyData/
│               ├── ClassA/
│               │      └── ins.mp4
│               ├── ClassB/
│               │      └── kep.mp4
│               ├── ClassC/
│               |      └── tak.mp4
│               ├── train.csv
│               ├── test.csv
│               ├── val.csv
│               └── classids.json
├── slowfast/
│       └── datasets/
│               ├── __init__.py
│               ├── mydata.py
│               └── ...
└── ...

As you can see, you will need to create a config file (.yaml), three files for the dataset split (.csv), a file for referencing classes (.json) and a file for dataset parsing (mydata.py).

To create the python file mydata.py, duplicate kinetics.py which is located in the same folder, rename it to mydata.py and replace all occurences of Kinetics by Mydata (search and replace, case-sensitive)

Once created, you will also need to import the newly created python file to the project by adding the line from .mydata import Mydata to the __init__.py file in the same folder.

The classids.json file contains a mapping of class names and ids. It will look like the following:

{"ClassA": 0, "ClassB": 1, "ClassC": 2}

The .csv files define which of the videos will be used for training, validation and inference testing, and which classes they reference.

They should look like the following:

/SlowFast/data/MyData/ClassA/ins.mp4 0
/SlowFast/data/MyData/ClassC/tak.mp4 2

For larger datasets, it will propably be the easiest to create files like this using an automated script that parses classids.json and the folder structure.

Note that all three files should not share any identical lines (use the same video) and pay attention to your actual paths (absolute or relative paths can be used).

Last but not least, you will need to create a configuration file to train or test the network. You can copy an existing one from the SlowFast/configs/Kinetics folder, e.g. I3D_8x8_R50.yaml.

In the copied file, replace all occurences of kinetics by mydata (case-sensitive).

You can run SlowFast with the new config that references your own dataset by running

python /SlowFast/tools/run_net.py --cfg /SlowFast/configs/MyData/I3D_8x8_R50.yaml

Note that you might need to adjust pathes to your actual working directory.

I hope this guide will help you guys using SlowFast with your own datasets 😃

Hi all,

After I have read this issue, I can not find the guide to create the “AVA-Dataset Format”. Apparently, AVA dataset needs human box and its corresponding action label. The discussions above are mainly focused on classifying the whole video.

Or have I missed anything? Maybe this issue #652 from mmaction2 is a related issue

Best!