| name | dataset-convert |
| description | Convert datasets between COCO, LabelMe, and YOLO pose formats using Annolid-native converters. |
| metadata | {"annolid":{"requires":{"bins":[]}}} |
Dataset Convert
Use this skill when the user asks to convert dataset formats.
Prefer the bundled converter script for deterministic runs:
python annolid/core/agent/skills/dataset-convert/scripts/convert_dataset.py ...
Supported conversions
labelme-to-coco: LabelMe directory -> COCO train/valid JSON.coco-to-labelme: COCO JSON (or annotations dir) -> LabelMe sidecar JSON next to images.coco-spec-to-yolo: COCO pose spec YAML -> YOLO pose dataset (data.yaml, images/, labels/).
Commands
LabelMe -> COCO keypoints
python annolid/core/agent/skills/dataset-convert/scripts/convert_dataset.py \
labelme-to-coco \
--input-dir /path/to/labelme_dataset \
--output-dir /path/to/coco_out \
--mode keypoints \
--train-valid-split 0.8
COCO annotations dir -> LabelMe dataset
python annolid/core/agent/skills/dataset-convert/scripts/convert_dataset.py \
coco-to-labelme \
--annotations-dir /path/to/coco/annotations \
--output-dir /path/to/labelme_out \
--images-dir /path/to/coco/images
COCO pose spec -> YOLO pose dataset
python annolid/core/agent/skills/dataset-convert/scripts/convert_dataset.py \
coco-spec-to-yolo \
--spec-yaml /path/to/coco_pose_spec.yaml \
--output-dir /path/to/yolo_pose_dataset
Notes
labelme-to-coco keypoints mode requires pycocotools.- For large datasets, prefer
--link-mode hardlink (default) to avoid image copying.
- Always return output paths and summary counts after conversion.
