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Converting COCO annotation (CVAT) to annotation for YOLO-seg (instance segmentation) and YOLO-obb (oriented bounding box detection)
| Date | Stars |
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| 2026-07-24 | 348 |
| 2026-07-25 | 348 |
| 2026-07-28 | 348 |
| 2026-07-30 | 348 |
| 2026-08-06 | 348 |
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# COCO to YOLO converter for instance segmentation (YOLOv8-seg) and oriented bounding box detection (YOLOv8-obb)
The repository allows converting annotations in COCO format to a format compatible with training YOLOv8-seg models (instance segmentation) and YOLOv8-obb models (rotated bounding box detection).
Key usage of the repository -> handling annotated **polygons** (or **rotated rectangles** in the case of YOLOv8-obb) exported from the CVAT application in COCO 1.0 format (with the save images mode set to True).
If you use it without CVAT, make sure that your COCO dataset folder has the following structure:
```
COCO_dataset/
|-- annotations/
| |-- instances_train.json
| |-- instances_val.json
|-- images/
| |-- image1.jpg
| |-- image2.jpg
| |-- ...
```
PS: For the instance segmentation task, Ultralytics YOLO12-seg, YOLO11-seg, YOLOv9-seg and YOLOv5-seg models are also supported (since they have similar annotation format to v8).
## Installation:
```
git clone https://github.com/Koldim2001/COCO_to_YOLOv8.git
cd COCO_to_YOLOv8
pip install -r requirements.txt
```
## How to run the code:
__Classic approach with pre-defined train/val/test split from CVAT (Tasks have a defined Subset in CVAT):__
```
python coco_to_yolo.py --coco_dataset="dataset_folder"
```
__Option with automatic split into train and val:__
```
python coco_to_yolo.py --coco_dataset="dataset_folder" --autosplit=True --percent_val=30
```
List of parameters with explanations that can be passed to the program before running it in the command line interface (CLI):
```
--coco_dataset TEXT Folder with COCO 1.0 format dataset (can be exported
from CVAT). Default is "COCO_dataset"
--yolo_dataset TEXT Folder with the resulting YOLOv8 format dataset.
Default is "YOLO_dataset"
--print_info BOOLEAN Enable/Disable processing log output mode. Default is
disabled
--autosplit BOOLEAN Enable/Disable automatic split into train/val. Default
is disabled (uses the CVAT annotations)
--percent_val FLOAT Percentage of data for validation when using
autosplit=True. Default is 25%
--help Show existing options for parsing arguments in the CLI
```
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# Russian Version of README:
Репозиторий позволяет преобразовать разметку формата COCO в формат, поддерживаемый для обучения моделей YOLOv8-seg (инстанс сегментация) и YOLOv8-obb (детекция повернутых боксов).
Ключевое применение репозитория -> работа с выгруженной разметкой **полигонов** (или **повернутых прямоугольников** в случае с YOLOv8-obb) из приложения CVAT в формате COCO 1.0 (с указанием режима save images = True).
Если же используете без CVAT, то убедитесь перед запуском, что ваша папка с COCO датасетом имеет такую структуру:
```
COCO_dataset/
|-- annotations/
| |-- instances_train.json
| |-- instances_val.json
|-- images/
| |-- image1.jpg
| |-- image2.jpg
| |-- ...
```
PS: Для задчи инстанс сегментации имеется также поддержка моделей Ultralytics YOLO12-seg, YOLO11-seg, YOLOv9-seg и YOLOv5-seg и других (так как у них аналогичная разметка с версией v8)
## Примеры использования:
Пример использования репозитория для задачи ***YOLOv8-seg*** представлен в видео на YouTube - [__ССЫЛКА__](https://www.youtube.com/watch?v=FF3mIWF0vFs&t=6s?t=34m49s) <br/>
Пример использования репозитория для задачи ***YOLOv8-obb*** представлен в видео на YouTube - [__ССЫЛКА__](https://www.youtube.com/watch?v=CZ_kZlto3IY&t=920s?t=20m2s)
## Установка:
```
git clone https://github.com/Koldim2001/COCO_to_YOLOv8.git
cd COCO_to_YOLOv8
pip install -r requirements.txt
```
## Как запускать код:
__Классический подход c предустановленным в CVAT разделением на train/val/test (у тасок определен Subset):__
```
python coco_to_yolo.py --coco_dataset="dataset_folder" --lang_ru=True
```
__Вариант с авторазделением на train и val:__
```
python coco_to_yolo.pExcerpt of 5,106 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bec5731b2b5632b4, topic:yolo, desc:instance segmentation, readme:instance segmentation
matched fp:bec5731b2b5632b4, topic:annotation-tool