Support for YOLOv11 in the conversion script

[ainayves]

Hello,

Does this conversion script support YOLOv11, or is it still limited to YOLOv8?

Thanks in advance for your response! 😊

[UltralyticsAssistant]

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[pderrenger]

@ainayves thanks for your question! The Ultralytics package supports YOLOv11, including model conversion/export to formats like ONNX and TensorRT. To use YOLOv11, simply specify model='yolov11' in your code. For details, see our Export documentation. Ensure you're using the latest ultralytics>=8.2.0 package.

[ainayves]

@pderrenger , Thank you for your response.

I realize my previous message was not very clear, and I apologize for that. My question is actually about the dataset format. If I’m not mistaken, this repository contains a script for converting COCO to YOLO. I would like to know if the dataset is converted to the YOLOv11 format, following a structure of this kind or another :

archive.zip/
├── obj.data
├── obj.names
├── obj__data
│ ├── image1.txt
│ └── image2.txt
└── train.txt # List of subset image paths

[pderrenger]

@ainayves the Ultralytics YOLOv11 dataset format uses a simplified structure with a data.yaml file and organized image/label directories, not the legacy obj.data/obj.names format. The converted COCO dataset will follow this structure:

dataset/
├── data.yaml
├── train/
│   ├── images/
│   └── labels/
├── val/
│   ├── images/
│   └── labels/

The convert_coco() function handles this conversion automatically. For details, see our Dataset Format Documentation and Converter Examples.

[glenn-jocher]

Thanks @ainayves. PR #129 updates the README to clarify current YOLO-format usage. For YOLOv11 and new projects, use the maintained ultralytics.data.converter.convert_coco API; legacy standalone conversion remains available for COCO and LabelMe workflows.