OCI Data Labeling for Machine Learning
OCI Data Labeling is a service that enables developers and data scientists to construct labelled datasets for training AI and machine learning models. By utilizing OCI Data Labeling, users can easily gather data, generate datasets, and assign labels to individual data records through user-friendly interfaces and public APIs. The labelled datasets can then be utilised to facilitate model development across a wide range of Oracle’s AI and data science services, ensuring a seamless and efficient model-building experience.
OCI Data Labeling allows developers and data scientists to label different types of data for training AI and machine learning models. Here are some examples of how OCI Data Labeling can be used for different types of data:
- Document Labeling
- Image Labeling
- Text Labeling
Typically the labelling of data is a manual task but there are also options to programmicly do this if the necessary label data is available.
The OCI Data Labeling service can be located under the Analytics & AI menu. See the image.
As we want to label a dataset, we need to first define the Dataset we want to use.
Select Datasets from the menu.
There are two options for creating the data set for labeling. The first is to use the Create Dataset option and the second is to Import Dataset.
If you already have your data in a Bucket, you can use both approaches. If you have a new dataset to import then use the Create Dataset option.
In this post, I’ll use the Create Dataset option and step through it.
Start by giving a name to the Dataset, then specify the type of data (Images, Text or Documents). In this example, we will work with image data.
Then select if the dataset (or each image) has one or multiple labels, or if you are going to draw bounding boxes for Object Detection.
For our example, select Single Label.
You can upload files from your computer or use files in an Object Bucket. As the dataset has already been loaded into a Bucket, we’ll select that option.
The Object Storage URL, Compartment and Namespace will be automatically populated for you.
Select the Bucket you want to use from the drop-down list. This dataset has 50 images each of Cats and Dogs.
The page will display the first eight or so, of the images in the Bucket. You can scroll through the others and this gives you a visual opportunity to make sure you are using the correct dataset.
Now you can define the Labels to use for the dataset. In our sample dataset we only have two possible labels. If your dataset has more than this just enter the name and present enter. The Lable will be created.
You can add and remove labels.
When finished click the Next button at the bottom of the screen.
The final part of this initial setup is to create the dataset by clicking on the Create button
When everything has been processed you will get a screen that looks like this.
You are now ready to move on to labelling the dataset.
To label each image, start by clicking on the first image. This will open a screen like what is shown (to the right).
Click on the radio group item for the label that best represents the image. In some scenarios maybe both labels are suitable, and in such cases just pick the most suitable one. In this example, I’ve selected Dog. An alternative approach is to use the bounding box labelling. I’ll have a different post illustrating that.
Select the most suitable label and then click ‘Save & next’ button.
Yes, you’ll need to do this for all the images in the dataset. Yes, this can take a lot of time, particularly if you have 100s, or 1000s of images. The Datasets screen has details of how many images have been labelled and or not, and you can easily search for unlabelled files and continue labelling, if you need to take a break.
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Ora-lytics 
November 11, 2023 at 12:35 pm
[…] a previous post, he gave examples of how to label data using OCI Data Labeling. It was a simple approach to data […]
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