Hi !
In my previous post I wrote about how to create a custom dataset with images to be used on a Azure Machine Learning Designer project. Today I’ll share the steps to use it.
Let’s start by creating a project based on the “Image Classification using DenseNet” template.
Next step is to select the compute instance we want to use in this Pipeline
Let’s delete the original animal datasource and add the newly created squirrel dataset.
Connect the new dataset with the [Convert to Image Directory] asset. To double check that we have the right contents, right-click on the Dataset asset and select [Preview Data], a list with the first 50 elements must appear on the preview window.
Now we can run the model. This may take a couple of minutes, so time for check twitter 😀
Once the pipeline run is done, we can check the score and metrics of the trained model. And, this is important for my online sessions, we can also download the trained model. Click on the [Train PyTorch Model] asset, select [Output and Logs] in the preview window, and we can download the file!
Tomorrow I’ll show how to create a HTTP Rest Endpoint to consume the generated model.
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El Bruno 