> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/datasets/dataset-versions/create-a-dataset-version.md).
# Create a Dataset Version
A version is a point-in-time snapshot of your dataset. We keep these versions since by keeping track of exactly which images, preprocessing, and augmentation steps were used in each iteration of your model, you maintain the ability to reproduce the results. This allows you to scientifically test across various models and frameworks while remaining confident that the results are attributable to the model changes and not due to a bug/change in the data pipeline.
Key Concepts: What are Workspaces & Projects?
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Once a version is created, it is frozen in time, which means changes to the project whether that be adding/removing images, annotations, or other data, won't affect versions that were created before.
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### How To Create a Dataset Version
To create a dataset version, click "Versions" in the sidebar associated with your Roboflow project. Then, click "Generate New Version".
From this page, you can set a train/test/valid split and specify preprocessing steps and augmentations for your new dataset version.
Once you have specified the preprocessing steps and augmentations you want to apply to your data, click "Generate". This will generate a new dataset version. You can then use this dataset version to train a model in Roboflow. You can also [export your dataset](/datasets/dataset-versions/exporting-data.md) for use in training a model manually.
### Readjusting Train/Validation/Test Splits
During the version creation process, you can also readjust the balance of your training, validation and test set splitting. To do this, go to "Step 2: Train/Test Split" and click the "Rebalance" button.
Set the train, validation, and test percentages, then choose how images are reassigned:
* "Move as few as possible" keeps images in their current split and only moves enough to reach the target percentages.
* "Random shuffle" randomly reassigns every image across the splits.
* "Random shuffle per upload batch or annotation job" shuffles each upload batch or completed annotation job separately, so every batch and job matches the target percentages.
You can also scope the rebalance to specific classes. Selecting classes limits moves to images that contain only those classes and switches the method to "Move as few as possible".
For large datasets, the rebalance runs as a background task with per-phase progress. You can dismiss the dialog while it continues, and track it in the Activity Center.