> For the complete documentation index, see [llms.txt](https://docs.binaexperts.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.binaexperts.com/datasets/health-check.md).

# Health Check

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Prefer to learn with video? We have a video tutorial that shows [how to use the dataset health check to improve model quality](https://youtu.be/aUFz6P4dtk4).
{% endhint %}

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;
* Object count histogram, and;
* 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:

<figure><img src="/files/6Q60Web7HmJBpyGf97XU" alt=""><figcaption></figcaption></figure>