Before you use Vision, your tenancy administrator must
set up the appropriate policies.
Setting Up the Policies 🔗
Follow these steps to set up the policies needed to use Vision.
In the Console navigation menu, select
Identity & Security.
Under Identity, select
Policies.
In the Policies page, select Create Policy.
The Create Policy panel is displayed.
Enter a Name. You can use alphanumeric characters,
hyphens, periods, and underscores only. Spaces aren't allowed. For example,
enter vision-access-policy.
Enter a Description to help other users know the purpose
of this set of policies. For example, enter Policy to access Vision
service..
Select the Compartment.
In Policy Builder, select Show manual
editor.
Add the following statement:
Copy
allow any-user to use ai-service-vision-family in tenancy
(Optional)
To limit access to your user group only, add the following policy
instead:
Copy
allow group <your-group-name> to use ai-service-vision-family in tenancy
Select Create.
1. Create a Project 🔗
A Project is a way to organize many models in the same workspace.
Creating a Project 🔗
Follow these steps to create a Project in Vision.
In the Console navigation menu, select
Analytics & AI.
Under AI Services, select
Vision.
From the Vision
Console page, under Custom
Models, select Projects.
The Project List page is displayed.
Select Create Project.
The Create Project panel is displayed.
Select a Compartment to create the Project in.
Give the Project a Name. For example
vision_demo. Don't enter confidential information.
(Optional)
Enter a Description for the Project to help others
identify it.
Select Create Project.
2. Create and Train a Custom Model 🔗
Follow these steps to create a custom model in your project.
Select a model type from the list. Figure 1. Select Model Type
Select the Training Dataset 🔗
If you have no annotated images, click Create a New
Dataset. You're taken to Oracle Cloud Infrastructure Data Labeling to create new dataset. More information on annotating images is available in the
Data Labeling
documentation.
If you have a dataset with annotated images, click
Choose Existing Dataset.
If you annotated the images in Data Labeling, click Data Labeling
Service and select your dataset file.
If you annotated the images in a third-party tool, click
Object Storage and select your dataset file.
Figure 2. Training Data Selections
Train the Custom Model 🔗
Enter a Name for the model.
Enter a Description so that you and other users can easily find
the model.
Specify a training duration. Figure 3. Training Durations
Select Next.
Review the model information.
Select Submit to start training the model.
What's Next 🔗
Now you know how to use Vision with custom models, try using
it with pretrained models.