Rendered.ai provides a service for generating analytics so users can learn additional insights about their datasets. Today there are three types of dataset analytics supported: Mean Brightness, Object Metrics and Properties. In this tutorial we will describe how to generate these analytics and review the output from each type of analytics.
Generating Dataset Analytics
We start by navigating to the Dataset Library page in the workspace that contains the dataset. Select the dataset then click the + icon next to Analytics in that dataset.
Next, we just need to choose a type of analytics we’d like to run on the dataset. Click Create to create the new analytics job.
A new analytics job is started, it will show the time dial symbol while the job is still running.
As a reminder, all dataset services share the same symbols for job status:
No symbol means that the service job is complete and ready to use.
The sand dial symbol means that the job is running. It will remain this way until the job has either completed or failed.
The error symbol means that the job has an issue. You can click on the symbol to fetch a log of the service to help determine what caused the issue.
Once complete, the symbol underneath Status will disappear and we will be able to download or go-to the Analytics. By clicking on the go-to symbol, it navigates us to the Analyses library with that analytics job selected.
The same process can be done to generate the other types of analytics. Below we’ll dig into what each of these types of analytics provides us.
Mean Brightness generates a plot of the “brightness” density which can be helpful in comparing one or more datasets.
Object Metrics generates some data on the types of objects in our imagery and two plots that indicate the size of bounding boxes and aspect ratio density of those bounding boxes.
The properties analytics type generates metrics on image counts, mean size and modes. It also provides helpful metrics on mean objects per image and annotation counts.