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Convert datasets between COCO, LabelMe, and YOLO pose formats using Annolid-native converters.
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Basado en la clasificación ocupacional SOC
Label video behavior segments from frame-grid images with a defined behavior list, model-ready JSON, resumable outputs, and no_behavior handling for sparse labels.
Use Annolid GUI tools for robust automation across web, PDF, video, and chat controls.
Infer the assay or paradigm from video context, task text, tracked entities, and experimental cues such as social interaction, open field, courtship, resident-intruder, and novel object recognition.
Choose assay-specific features and measurable objectives for behavior analysis, including distances, zones, speed, contact, orientation, and object interaction.
Segment behavior timelines from tracks, pose, contact, speed, and proximity signals into typed intervals with stable labels and rationales.
Keep behavior analysis aligned with assay objectives, controls, reproducibility, and measurable outputs instead of ad hoc summaries.
| name | dataset-convert |
| description | Convert datasets between COCO, LabelMe, and YOLO pose formats using Annolid-native converters. |
| metadata | {"annolid":{"requires":{"bins":[]}}} |
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 ...
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/).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
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
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
labelme-to-coco keypoints mode requires pycocotools.--link-mode hardlink (default) to avoid image copying.