Outlier Detection with CIFAR10 Image Classifier
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
NoteThis demo requires Knative installation on the cluster as the outlier detector will be installed as a kservice.
Use the following model url with
tensorflow runtime. Set the protocol to ‘tensorflow’:
Setup Outlier detector
Setup an outlier detector with model name
cifar10 using the default settings (which sets Reply URL as seldon-request-logger in the logger’s default namespace - change if you modified this at install time) and storage URI as follows:
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.
View 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.
Monitor outliers on the Monitor Screen
Under the ‘Monitor’ section you can see a timeline of outlier requests.