Dataset export
How to Export COCO, VOC, and YOLO Datasets
This guide helps you choose the right downstream format first, then run the most important checks after export.
6 minCOCOVOCYOLOexport
Let downstream needs choose the format
Format selection should not be based on preference alone. It should follow the training script, customer, or partner requirement.
YOLO is common in detection training.
VOC is common in XML-based pipelines.
COCO is common in richer data structures.
For YOLO, inspect folders and class order
The most common YOLO problem is mismatch between the export and the training configuration.
Check the images and labels folders.
Confirm class order.
Open a few TXT labels randomly.
VOC and COCO depend more on schema integrity
VOC depends on XML completeness, while COCO depends on correct relations between categories, images, and annotations.
Let the target system load a sample first.
Do not stop at “export succeeded.”
Finish schema checks before handoff.
FAQ
Which format is best? There is no universal best format. The right answer depends on the downstream system.
What should I inspect first after export? Check the structure first, then inspect random samples.
Continue with these guides
Suggested next learning steps
If you are new, start with annotation and export guides.
If you are preparing team workflows, continue with collaboration and plan-selection guides.
If you want the full workflow, move into OpenClaw and training guides next.
Next step
Move from content into product action
If this guide already solved the current question, use the entry points below to continue with the actual task.