how to monitor object detection model in production?

Question

I have a doubt, can someone tell me how to monitor the object detection model in production, relevant articles would be helpful.

I have build FasterRCNN using PyTorch, I’m moving my model to production I would like to know which statistical and which parameter we consider to monitor the model or retrain the model.

Earlier, I have productionized regression model into production and I have used statistical test to monitor the data drift and model drift.

can someone tell me how to monitor the object detection model?

Thanks in advance

Answer 1

In technical terminology, we refer to this as out-of-distribution detection or anomaly detection. Using a reference dataset of images, you can perform ongoing monitoring of all new images to understand which ones are similar to the training data and which ones seem like anomalies.

It is essential to know exactly when your model won’t generalize to new settings. For example, if your object detection model was trained primarily on images from outdoor locations in good weather and good lighting, it will likely underperform in rainy and dark conditions. you need a data drift detection tool that can automatically detect if these dissimilar images start coming in.