How do I use the Object Detection API to evaulate an own custom model? What do I write into the config files?

Question

I have a custom object detection model that I can call with model = MyModel() and model.loadweights(checkpoint) and I want to evaluate it using the Object Detection API.

From what I understood there are two possibilities, either I use the legacy eval.py, there I don't know, what to put into the pipeline_config file

Or I use the newer version that is implemented in model_main_tf2.py, but there I would have to save my model as model.config and I don't know what to put the pipeline file either.

Since my model is a YOLO model, it is not included in the sample once yet.

https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2

Would really appreciate the help!

Answer 1

You can't calculate the mAP using the Object Detection API because there's no pipeline.config file for Yolo.

However, you can check this repo out. It's a Tensorflow based implementation of YoloV3. They have working code for calculating mAP. You can modify this accordingly to calculate the mAP of your model.