I’m a bit out of my depth here. I know just enough to use tensorflow’s object detection api to do transfer learning on a model from the model zoo, and train a custom object detector. What I’d like to do is add a confusion matrix to the tensorboard visualization I get when I run the main training script in the model garden repository. Currently, I get a bunch of scalar graphs (mAP, AR@K , loss) and images (visualization of model output vs gt). I don’t think I did anything special to create these visualizations. I assume they’re implemented somewhere, and i’m too new to know where. I found a pretty concise tutorial for adding a confusion matrix visualization to tensorboard here (https://towardsdatascience.com/exploring-confusion-matrix-evolution-on-tensorboard-e66b39f4ac12), but I don’t feel like I understand where I would ‘intervene’ or inject my changes into the model_main training script that i’m using (this is the one found in models/research/object_detection of tf’s ‘model garden’). If anyone has had a similar case, or knows of a demo that I might be able to follow I’d be grateful.