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by Mark Sturdevant | Updated June 17, 2019 - Published June 14, 2019
AnalyticsArtificial intelligenceData scienceDeep learningMachine learningPythonVisual recognition
This code pattern is part of the Getting started with PowerAI Vision learning path.
After a deep learning computer vision model is trained and deployed, it is often necessary to periodically (or continuously) evaluate the model with new test data. This developer code pattern provides a Jupyter Notebook that will take test images with known “ground-truth” categories and evaluate the inference results versus the truth.
We will use a Jupyter Notebook to evaluate a PowerAI Vision image classification model. You can train a model using the provided example or test your own deployed model. The notebook will use test images, which are separated into directories to indicate the expected category classifications. These expected classifications are considered the “ground truth.” The deployed model’s API endpoint will be called to collect the inference results for each image. The collected results are then used to evaluate the actual model performance. Model accuracy is demonstrated by showing a confusion matrix and calculating a variety of the most common statistics used to measure the accuracy of a model.
When you have completed this code pattern, you will understand how to:
The steps will show you how to:
Find the detailed steps for this pattern in the README file. Learn how to:
This code pattern used a Jupyter Notebook to evaluate a PowerAI Vision image classification model by taking test images with known “ground-truth” categories and evaluating the inference results versus the truth. The code pattern is the final part of the Getting started with PowerAI Vision learning path. Congratulations! You should now have a fundamental understanding of PowerAI Vision and some of its advanced features. But, if you want to learn more, take a look at the PowerAI Vision page.
Learn how to build and deploy a model using PowerAI Vision and then integrate it into an iOS application.
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