I have a group of images with ground truth detection boxes and I want to simply run them through a pre-trained model from the Model Zoo and get the, say, precision/recall/mAP between the ground truth boxes and predicted detections. But all the evaluation methods I've seen use training/validation data that I never specified in the first place because I'm using a pre-trained model.
It seems like a simple question, but I can't seem to find an answer. How can I go about doing this?
If your images happen to be of something that one of the models in the model zoo is already trained on, then you could run inference on them and get predictions.
However, to get meaningful metrics on a custom dataset, you will have to do some training; starting with one of the models in the model zoo is a great idea. Here are a couple tutorials that might help:
https://pythonprogramming.net/custom-objects-tracking-tensorflow-object-detection-api-tutorial/
https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/
