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Image Data Outlier Detection
Launch an image classifier model and detect outlier prediction requests.
This demo is based on VAE outlier detection in the alibi detect project. Here we will :
- Launch an image classifier model trianed on the CIFAR10 dataset
- Setup an outlier detector for this particular model
- Send a request to get a image classification
- Send a perturbed request to get a outlier detection
Note
This demo requires Knative installation on the cluster as the outlier detector will be installed as a kservice.Create Model
Use the following model url with tensorflow runtime:
gs://seldon-models/tfserving/cifar10/resnet32
Setup Outlier detector
Setup an outlier detector with model name cifar10 using the default settings and storage URI as follows:
gs://seldon-models/alibi-detect/od/OutlierVAE/cifar10
Make Predictions
Run a single prediction using the tensorflow payload format of an image truck. Also a perturbed image of the truck in the same format at outlier truck image. Make a couple of these requests at random using the predict tool in the UI.
Monitor outliers on the Requests Screen
Go to the requests screen to view all the historical requests. You can see the outlier value on each instance. Also you can highlight outliers based on this score and also use the filter to see only the outliers as needed.
Delete Deployment
Finally, delete the deployment. This will also remove the outlier detector created for the deployment.
Last modified April 20, 2020: Adding outlier detection demo with image data (5edb87f)