Health Check

Assess and improve the quality of your dataset.

Prefer to learn with video? We have a video tutorial that shows how to use the dataset health check to improve model quality.

Health Check shows a range of statistics about the dataset associated with a project. You can see the following pieces of information:

  • Number of images in your dataset;

  • Number of annotations;

  • Average image size;

  • Median image ratio;

  • Number of missing annotations;

  • Number of null annotations;

  • Image dimensions across your dataset;

  • A heatmap of annotation locations.

Using health check, you can derive a range of insights about your dataset. For example, if you have no null annotations, you may want to consider adding a few depending on the project on which you are working; if there are images with missing annotations, you can dig deeper to add the requisite annotations.

See more on the difference between null and missing annotations.

Class Balance

The health check feature also shows class balance across your annotations. Class Balance shows how many of each object there are and easily visualizes class balance/imbalance. Imbalanced data can yield unfavorable results, especially when measuring models with accuracy.

Here is an example of the class balance feature in use:

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