The gap between a labeled dataset and a trained model is smaller than most teams think.
But the step that trips teams up most often is not labeling. It is the split.
Someone has a finished COCO JSON with 5,000 images and 15,000 annotations. The training script expects three separate files: train, val, and test.
The common fix is a 20-line Python script. That script usually works, but it does not handle stratification, it is not reproducible across runs, and nobody wants to debug it when the dataset structure changes.
If you want the fastest path, open the free tools section and launch the Dataset Splitter directly.
Short answer
Use a dataset splitter when you have a complete annotation file and the next step expects separate train, validation, and test subsets.
A good splitter should:
- accept a standard annotation format (COCO JSON)
- let you configure the ratio (e.g. 70/20/10)
- support stratified sampling to preserve class balance
- produce separate files that are each valid and ready for training
- run without login, signup, or server-side processing
The Dataset Splitter does all of this directly in your browser. Nothing is uploaded to a server.
Why splitting matters
An unbalanced or careless split creates problems that surface late:
- validation metrics that do not reflect real performance
- test sets that accidentally exclude rare classes
- training sets that overfit to an unrepresentative sample
- inconsistent results when the split is randomized differently each run
These problems are silent. The pipeline runs, the model trains, but the metrics are unreliable because the split itself was unreliable.
Random split vs. stratified split
A random split shuffles all images and divides them by ratio. This works well when the dataset is large and class distribution is roughly uniform.
A stratified split groups images by their primary class label and then splits within each group. This preserves the class distribution across all three subsets.
Stratified splitting matters when:
- you have imbalanced classes (e.g. 80% "car", 20% "person")
- rare classes might end up entirely in one split
- your evaluation metrics are sensitive to class coverage
The Dataset Splitter supports both modes with a single toggle.
How the splitter works
The workflow is:
- upload one COCO JSON annotation file
- adjust the train/validation/test ratio using the sliders
- toggle stratified mode if needed
- click split
- download each file individually or all at once
Each output file is a valid COCO JSON with the same category definitions. Only the image and annotation assignments change across splits.
The split uses a seeded pseudo-random shuffle, which means the same file with the same seed produces the same split every time.
What to check after splitting
After splitting:
- verify that the total image count across all three files matches the original
- check that rare classes appear in both the training and validation sets
- confirm that each output file parses cleanly in your training framework
- run a Dataset Health Report on the training split to catch class imbalance before training starts
Common split ratios
| Ratio | When to use |
|---|---|
| 70/20/10 | General-purpose default for most detection tasks |
| 80/10/10 | When the dataset is smaller and you want more training data |
| 60/20/20 | When you need stronger validation and test signals |
| 90/5/5 | Large datasets where even 5% gives a meaningful evaluation set |
The splitter supports any ratio combination, including edge cases like 100/0/0 (no split) or 50/50/0 (no test set).
A practical pre-training workflow
The strongest pre-training workflow combines three free tools:
- Format Converter if the source format is not COCO JSON
- Dataset Splitter to create train/val/test sets
- Dataset Health Report to validate the training split
That sequence answers three questions before a single GPU cycle:
- Is the format right for the training framework?
- Is the split reasonable and reproducible?
- Is the training data healthy and balanced?
Privacy and security
The splitter runs entirely in the browser. No file data is uploaded to any server. The annotation file is read, split, and downloaded locally.
This makes the tool safe for:
- sensitive or proprietary datasets
- HIPAA-adjacent medical imaging workflows
- teams with strict data residency requirements
- quick evaluations before committing to a platform
Where LabelOp fits
LabelOp exposes a public pre-training toolkit:
- Free tools section for the entry point
- Annotation Format Converter for format normalization
- Annotation Merger for fragmented file cleanup
- Dataset Splitter for train/val/test preparation
- Dataset Health Report for file-level QA
For full project workflows with team collaboration, versioning, and AI-assisted labeling, the dashboard handles the rest.
Related reading
- Annotation Format Converter for Computer Vision Teams in 2026
- Annotation Merger Tool for Computer Vision Teams in 2026
- Dataset Health Report for Computer Vision Teams in 2026
- Dataset Split Planning Before Labeling in 2026
References
FAQ
Can I split formats other than COCO JSON?
Currently the splitter supports COCO JSON only. If your annotations are in a different format, convert them first using the Format Converter.
Is the split reproducible?
Yes. The splitter uses a fixed seed (42) for the pseudo-random shuffle. The same file produces the same split every time.
Does splitting upload my data to a server?
No. Everything runs in your browser. No data leaves your machine.
What happens to images with no annotations?
They are still distributed across splits. In stratified mode, unannotated images are grouped together and split proportionally.
Can I split by image folder or filename pattern?
Not currently. The splitter works at the COCO image-level, not at the filesystem level. All images in the JSON are split regardless of their file paths.