How to create an experiment of Images with Studio9

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Hey Readers, Today, I am here with another blog. We will learn something special about creating an AL/ML model using Images. To perform this operation, Knoldus made a project which is known as Studio9.

First, we will know what is studio 9?

Studio 9 platform Enables developers to create, train and test the ML models quickly with a single click of a button along with other features like pipeline and custom pipeline creation using Jupiter lab.

In studio 9, We can create a model using images, CSV, and video. So in this blog, We will see how we can create a model using images. To perform this operation, We need a few files. If we are creating a new model then we need to have two files – train and test because We will train a model on the basis of an image file which is called a trained file.

After the creation of a new model, We can train any other data on the basis of that model. I have divided this process into steps. You can follow these steps to create a model:-

Step 1:

You need to log in to studio9. If you have a login ID otherwise you can click on the sign-in button and fill in the required details and create a user for studio9.

Step 2:

Create an Album and upload the data – Train and test data:

Just simply click create Album on the left-side and  fill in the required details as per the requirement as below:

Actually, You can upload the data from the local system but  You can select one image at a time so for bulk data we are using an AWS s3 bucket and also provide the access, secret key, and session token. You can use the below command to generate token and keys:


 aws sts assume-role –role-arn

<Arn of your s3>   –role-session-name s3-access –profile <Your aws configure profile name > –duration-seconds 20000


aws sts assume-role –role-arn arn:aws:iam::25235823423:role/s3-access   –role-session-name s3-access –profile project –duration-seconds 20000

Before the upload your data to the album. You need to provide the labels of the images. Actually, We can use two types of data.

  1. The data have already been labeled with images.
  2. When data has no label so we can upload the data from local or s3.

Now click on the upload button. We need to follow the same step to upload test data.

Step 3:

Navigate to the lab section on the top  page:

Now, You can fill in the required details like experiment name, experiment type – We will select CV TL train because We are using image data so We will select Computer vision, Training mode – We can select 1 step or 2 steps as per requirement, UTLP- select an algorithm, Model type

– We will select a classifier because we are using image data. And upload the train and test data.

Now you will see an Advanced option just below the train data option. If you check the Enable Automated Data Augmentation

 Then It will generate more images by applying augmentation.

In the last just click on Create.

Hurry!!, Your CV model is created. Now you can use this model to train other data. You can find your model in the cv model option on the left side.


We have seen how we can create an AI/ML model using images with the help of studio9. That model can be use further to train other data.

Studio9 Login Screenshot

Studio9 is an open source platform for doing collaborative Data Management & AI/ML anywhere Whether your data is trapped in silos or you’re generating data at the edge, Studio9 gives you the flexibility to create AI and data engineering pipelines wherever your data is. And you can share your AI, Data, and Pipelines with anyone anywhere. With Studio9, you can achieve newfound agility to effortlessly move between compute environments, while all your data and your work replicates automatically to wherever you want.

Studio9 Create Experiments Screenshot

We can simply create a model using image data. Simply upload the test and train data and apply our algorithm to data then wait for a while. You will get a trained model. With the use of this model, You can train any data.

Written by 

Rahul Miglani is Vice President at Knoldus and heads the DevOps Practice. He is a DevOps evangelist with a keen focus to build deep relationships with senior technical individuals as well as pre-sales from customers all over the globe to enable them to be DevOps and cloud advocates and help them achieve their automation journey. He also acts as a technical liaison between customers, service engineering teams, and the DevOps community as a whole. Rahul works with customers with the goal of making them solid references on the Cloud container services platforms and also participates as a thought leader in the docker, Kubernetes, container, cloud, and DevOps community. His proficiency includes rich experience in highly optimized, highly available architectural decision-making with an inclination towards logging, monitoring, security, governance, and visualization.