---

name: dataset-ops description: "Move CVAT data between tasks, projects, dataset formats, manifests, backups, frames, and optional PyTorch dataset adapters." metadata: disco-role: operating disable-model-invocation: true license: MIT

CVAT dataset operations

Use this sub-skill when the user asks how to import/export annotations or datasets, choose a CVAT-supported format, include or exclude images, prepare local/remote/share/cloud data sources, create dataset manifests, download frames, back up projects/tasks, convert source data before upload, or consume CVAT data through SDK/PyTorch dataset adapters.

Route first

• Read references/formats-and-data-flows.md to select formats and map import/export operations to CLI and SDK calls.
• Read references/data-preparation.md for local/remote/share/cloud inputs, manifests, DICOM-to-image preparation, frame extraction, and validation before upload.
• Read references/pytorch-datasets.md for cvat_sdk.datasets and optional cvat_sdk.pytorch usage.
• Read references/troubleshooting.md for format/label/archive/image/mask/video-track/cache errors.
• Use scripts/manifest_command_builder.py to generate safe dataset-manifest commands without depending on the original repository utility script.

Choose the interface

• Terminal automation: use ../cli-automation/SKILL.md and its command builder.
• Python automation: use ../sdk-automation/SKILL.md for task/project objects and import/export methods.
• Model-assisted annotation: route to ../auto-annotation/SKILL.md.
• Server storage, Docker, Helm, browser access, or deployment storage configuration: route to ../deployment-admin/SKILL.md.

Common task/project data commands

# Export task annotations only.
cvat-cli --profile prod task export-dataset --format "COCO 1.0" --with-images no 42 task-coco.zip

# Export task with images when a downstream tool needs complete data.
cvat-cli --profile prod task export-dataset --format "YOLO 1.1" --with-images yes 42 task-yolo.zip

# Import annotations into an existing task.
cvat-cli --profile prod task import-dataset --format "CVAT 1.1" 42 annotations.zip

# Create project tasks from a dataset archive.
cvat-cli --profile prod project import-dataset --format "Datumaro 1.0" 7 dataset.zip

# Back up for CVAT-to-CVAT restore.
cvat-cli --profile prod task backup 42 task-backup.zip

The Python SDK equivalents are Task.import_annotations(), Task.export_dataset(), Project.import_dataset(), Project.export_dataset(), download_backup(), and create_from_backup().

Format selection rules

• Use CVAT 1.1 / CVAT for images 1.1 / CVAT for video 1.1 for round-tripping CVAT annotations with maximum CVAT fidelity.
• Use COCO 1.0 for common detection/segmentation pipelines with bounding boxes, polygons, masks, and categories.
• Use YOLO 1.1 or Ultralytics YOLO variants for YOLO training pipelines; verify whether the task is detection, segmentation, pose, oriented boxes, or classification.
• Use Datumaro 1.0 when interoperating with Datumaro-style conversion pipelines.
• Use format-specific references for KITTI, MOT/MOTS, Pascal VOC, Open Images, Cityscapes, CamVid, ImageNet, LabelMe, WIDER Face, VGGFace2, LFW, ICDAR, Market-1501, and segmentation masks.

Always verify shape types, attributes, tracks, video support, and whether images are included before choosing a format.