Get optimal results with your brand-new trained algorithm application by learning how to carry out actual image analyses with it.
In the Introduction to IKOSA AI section, you have been acquainted with the process of successfully training a custom image analysis algorithmapplication. Yet, in order to perform actual analysis tasks, you have to first prepare the algorithm model for examining novel image data.
In this article, you will learn how to deploy your custom IKOSA AI algorithms applications and subsequently analyze new image data with them.
What to expect from this page?
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📑 Get started
Go tothe
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“IKOSA AI Dashboard”.
Click the
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“Training Overview” item or
Go to the “Latest Training Sessions” list.
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📑 Deploy/Undeploy your trained
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model
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Initially, your custom algorithm application will be in an undeployed mode with the status “successful.”
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“successful”.
For subsequently applying your custom algorithm application to new image data:
Choose “Deploy” from the hamburger button.
When the status “deployed” is displayed, the status indicator switches from green to blue, and your
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application is ready for use.
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Info |
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Please note: In order to Depending on your software package, you will be able to deploy another trained algorithm, you have to first underlay the one currently displayed. To do this, choose Undeploy train and activate (deploy) a certain number of apps. If you need to deploy an app but the the maximum number if already activated, Undeploy one of the appsfrom the hamburger Button and proceed deploying the new algorithm app as shown above. |
📑 Use your
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application on “unseen” images
The term “unseen images” refers to image data that is novel to your trained algorithmmodel. To run your algorithm application training on novel image data follow our step-by-step guidelines.
Step 1 -
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Navigate to the
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images for analysis with your IKOSA AI app
Once your algorithm application is deployed, switch to the respective “Image Library” of the current project:
Option 1:
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Go to the “Training Overview” page.
Click the “View your application” link.
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Navigate to the “Image Library” page.
A window with images for analysis opens.
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Option 2:
Go to the main “Dashboard”.
Click the “Projects” list item.
Select the project for which you trained your
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application.
Navigate to the “Image Library” page.
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Step 2 - Select images and start the analysis
From the “Image Library” page |
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From the “Image Viewer” |
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Please note: It is best to use images that were not part of the training/validation set.
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Start image analysis directly from the “Image Viewer”.
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?? Collect your results
Step 1
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Navigate to the “Image Analysis” page either by:
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You will then be redirected to the “Image Analysis” page.
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Navigate to the Analysis
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Jobs
Once your analysis job is done, it will be displayed in the Finished tab.
Depending on the complexity of your algorithmtrained model, the analysis might take a few moments in which the job will be in an Active state.
If your analysis job is not finished yet, you can refresh the analysis jobs results section anytime by clicking the “Refresh” button.
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.
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Step
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2 - Download your results
Get all result outputs fora single image by clicking the “Download” icon next to it.
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Get only an individual output file fora single image by opening the drop-down menu next to it and downloading the desired file.
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Get all result outputs fora single image by clicking the “Download” icon next to it.
Get a zipped file containing the result outputs for all images in the project by clicking the “Download all” button above the table.
download all.
mp4Get a zipped file containing the result outputs for all images in the project merged in a single CSV-file by clicking the “Download all merged” button above the table.
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Info |
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Please note: For quantitative analysis you will be presented .csv or .xlsx files and .jpg files with visualizations. |
You have done a great job obtaining solid numeric data from your images!
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If you still have questions regarding your algorithm application training, feel free to send us an email at support@ikosa.ai. Copy-paste your training ID in the subject line of your email.
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