Data Driven Series: A Comprehensive Guide to Oracle Data Labelling Service: Part 3

on

Author: Phil Godfrey

In the previous blog throughout this series, we introduced the Data Labelling service, explaining what it is, any necessary pre-requisites to enable the service. The following blog then explored creating a custom dataset of images (car parks and spaces).

The next blog in the series will work through labelling the generated records (images) within a dataset within Oracle Data Labelling.

What is Data Labelling?

Data Labelling is the process of “identifying properties (labels) of documents, text, and images, and annotating them (labelling)”.

What are some examples?

What you can label is almost endless, it could be in the format of text, documents and even images.

  • The topic of a news article
  • The sentiment of a tweet
  • Objects identified within an image
  • and many more

 

Data Labelling Service

The OCI Data Labelling service is an OCI native service that allows customers and business users to leverage labelling functionality. This includes utilizing built-in functionality to

  • Create and browse datasets
  • View data records (text, images)
  • Apply labels for the purposes of building AI/ML models.

The service also provides interactive user interfaces designed to aid in the labelling process, with an interactive user interface to draw bounding boxes used for object detection within images.

Accessing the Data Labelling Service

In OCI, navigate to Analytics & AI, and under the Machine Learning subheading, you will find Data Labeling.



In the previous blog, we created a dataset for the Data Science department, who are working on a model to predict car park spaces in near real-time for a live-traffic app.

For this we can create a custom dataset, using images of a car park, to allow us to annotate these records to identify:

  • Cars
  • Spaces



Once we’d worked through the relevant steps, the dataset Parking Spaces was created.



 

Next, we will explore the labelling service in more detail by annotating our imagery data to create our labelled dataset.

Annotating Labels

Here we take each of the data records we’ve created in the previous steps and label each one. In our example we will be looking to identify and assign two data labels, cars and spaces. An example of this is below:




We utilize the Data Labelling service to draw bounding boxes around each object of interest, in theory to match the above. Green boxes would be labelled cars, and orange boxes would be labelled spaces.

Within the data labelling service, it looks like the below.



If there are no cars or parking spaces within the image, you can skip the image, and it will remain unlabelled.




Now we have a fully labelled dataset, which we can then utilise across other Oracle services, such as Vision, to create a custom model based on our labelled dataset. We can see an overview within the Data Labelling dataset tab.




This dataset can now be referenced in other areas of OCI, including in the creation of Custom Vision models created in OCI. 

Keep your eyes peeled for the next blog series which will go through creating a custom vision model step-by-step.


Comments

Post a Comment