I have trained a pre-trained RESNET18 model in pytorch and saved it. While testing the model is giving different accuracy for different mini-batch size. Does anyone know why?
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Yes, I think so. RESNET contains batch normalisation layers. At evaluation time you need to fix these; otherwise the running means are continuously being adjusted after processing each batch hence giving you different accuracy .
Try setting:
before evaluation. Note before getting back into training, call
model.train().