Dataset versions and exports

A dataset version prepares selected project data for a repeatable download or model workflow. An export then represents that version in a chosen dataset format.

Build a version

Demo data · Inspect the version's images and split allocation before generating the format your training tool expects.

Give versions names that describe their purpose: a reviewed batch, a different image size, or a new augmentation setup. The editable project remains your working collection; versions are prepared inputs for training and download.

1. Open the project's Export Data section and create a version.
2. Review the selected data and filters.
3. Choose the split strategy and train, validation, and test allocation.
4. Add preprocessing steps in the order you need them.
5. Add augmentations and review their settings and generation factor.
6. Preview representative images, then submit the version build.
7. Wait for the version's terminal status and inspect warnings or failures.

Preprocessing prepares the input; augmentation creates training variation. Changing their order can change both pixels and geometry.

Understand previews

Choose variation that reflects situations your model should handle. Lighting changes may help camera inspection; flipping text may make OCR examples less realistic. Pose datasets need meaningful left/right pairs. Start simple, then add variation and compare on the same held-out data.

A composed preview processes the example through the selected step. Editing a step includes its preceding steps, not later steps. Augmentation previews also include the preprocessing prefix.

The preview is a seeded example, not a promise that every full-resolution generated image will have identical random pixels. Filtering can remove examples, and expansion steps can produce several examples. Observe any truncation or resource-limit message instead of assuming all outputs are shown.

Generate an export

Match a format to your task

Choose the format your trainer or labeling tool expects. The format menu and summary show the exact choices for your data.

Generate and inspect the files

Demo data · The completed export card reports source records, image files, exported labels, and skipped kinds. Open its details before downloading or starting training.

1. Choose a completed version.
2. Select a format suitable for your trainer or labeling tool.
3. Review format-specific options, such as image conversion or box conversion.
4. Generate the export and wait for completion.
5. Inspect its summary and download the archive.

Different formats preserve different geometry. A detection format may use boxes where the source contains polygons; a format without a particular annotation type may omit it. Review supported, exported, and skipped counts rather than assuming every source annotation is represented.

Source records and image files are different counts: some formats can produce multiple crops from one input. Skipped reasons can overlap, so adding those counters does not give a count of unique failed images. Open the archive and check a representative image and label together when validating a new export setup.

Classification exports require meaningful image-level classes. Mask exports and keypoint exports have their own class and geometry requirements. For custom metadata, verify the intended consumer understands the exported extension fields.

Failed, queued, or recovery-required exports

Use the existing export's status and controls. A recovery-required export needs status inspection or operator help; creating another generation is not a safe substitute for resolving an uncertain publication. Generation allowances can still apply to submitted attempts.

Deleting an export is separate from deleting the source version or project. Keep a downloaded copy when needed, and wait for scoped cleanup to finish before assuming storage is released.

Next: Model training or SDK downloads.