I am training a ResNet50 network with simulation data and my validation dataset is the experimental data. The simulation data is not a 100% accurate representation of the experimental data. The purpose of this network is for binary classifier. I notice something very strange at the initial state of training as following:
The training cross-entropy loss is ~0.69, which is roughly equal to -log(50%) and the accuracy is ~50%. This logically makes sense, because the model basically hasn't learned anything and is just randomly guessing. The loss on the validation (experimental) dataset is also ~0.69, however, the accuracy is either very close to 0% or 100%. I understand this is partially caused by the difference between simulation (training) and experimental (validation) data, but it might be telling something deeper than that, for example, how the simulation dataset is different from experimental data. I couldn't figure it out, and I would love to hear any opinion. Please refer to the metrics below.