HP INCFollow


A machine learning model is given for classifying images according to different aspects.

Even though this model had a good accuracy on the test set, when it was deployed into

production, we (the model developers) started to face many cases with failures due to input

images that were not covered in our training or test sets. We’ve identified that these failures

commonly have smaller confidence levels, and hence applying a threshold value to exclude low

confidence predictions could work as a solution to avoid failures. In order to establish this

threshold value, we explored different threshold values with the confidence scores returned by

the model on different subsets of images. When below the defined threshold, the output is then

considered unknown (i.e., not belonging to any of the established classes) and the SDK client will

be able to define a specific behavior for these cases. Also, the threshold can be configured to be

adaptive regarding each of the classes that the model was trained to output.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.