third_party/mmyolo/docs/en/useful_tools/dataset_converters.md

Dataset Conversion

The folder tools/data_converters currently contains ballon2coco.py, yolo2coco.py, and labelme2coco.py - three dataset conversion tools.

• ballon2coco.py converts the balloon dataset (this small dataset is for starters only) to COCO format.

python tools/dataset_converters/balloon2coco.py

• yolo2coco.py converts a dataset from yolo-style .txt format to COCO format, please use it as follows:

python tools/dataset_converters/yolo2coco.py /path/to/the/root/dir/of/your_dataset

Instructions:

1. image_dir is the root directory of the yolo-style dataset you need to pass to the script, which should contain images, labels, and classes.txt. classes.txt is the class declaration corresponding to the current dataset. One class a line. The structure of the root directory should be formatted as this example shows:

.
└── $ROOT_PATH
    ├── classes.txt
    ├── labels
    │    ├── a.txt
    │    ├── b.txt
    │    └── ...
    ├── images
    │    ├── a.jpg
    │    ├── b.png
    │    └── ...
    └── ...

2. The script will automatically check if train.txt, val.txt, and test.txt have already existed under image_dir. If these files are located, the script will organize the dataset accordingly. Otherwise, the script will convert the dataset into one file. The image paths in these files must be ABSOLUTE paths.
3. By default, the script will create a folder called annotations in the image_dir directory which stores the converted JSON file. If train.txt, val.txt, and test.txt are not found, the output file is result.json. Otherwise, the corresponding JSON file will be generated, named as train.json, val.json, and test.json. The annotations folder may look similar to this:

.
└── $ROOT_PATH
    ├── annotations
    │    ├── result.json
    │    └── ...
    ├── classes.txt
    ├── labels
    │    ├── a.txt
    │    ├── b.txt
    │    └── ...
    ├── images
    │    ├── a.jpg
    │    ├── b.png
    │    └── ...
    └── ...