Built for ML engineers who need to move fast between frameworks.
100 concurrent connections download your images in parallel. Built with Rust and Tokio for maximum throughput.
From YOLO's to COCO JSON to Pascal VOC. Supports detection, segmentation, pose estimation, and classification.
Everything runs on your machine. No data is ever sent to external servers. Your annotations stay yours.
Three steps. One ZIP file. Done.
Pick your .ndjson or .jsonl export file from your YOLO's project.
Select from 12 target formats including YOLO's, COCO, Pascal VOC, and more.
Get a complete ZIP with converted annotations, images, and config files.
All the formats you need for modern ML pipelines.
TXT annotations and YAML config used with YOLO26.
TXT annotations and YAML config used with YOLOv12.
TXT annotations and YAML config used with YOLO11.
TXT annotations and YAML config used with YOLOv9.
TXT annotations and YAML config used with YOLOv8.
TXT annotations and YAML config used with YOLOv7.
TXT annotations and YAML config used with YOLOv5.
Darknet TXT annotations used with YOLO Darknet (both v3 and v4) and YOLOv3 PyTorch.
COCO JSON annotations are used with EfficientDet Pytorch and Detectron 2.
Common XML annotation format for local data munging (pioneered by ImageNet).
TFRecord binary format used for both Tensorflow 1.5 and Tensorflow 2.0 Object Detection models.
CreateML JSON format is used with Apple’s CreateML and Turi Create tools.