Batch size Having significant impact on the performance of VGG16

I was using pytorch's implementation of VGG16 with the pretrained weights I get when weights='DEFAULT'. i.e is the imagenet1k weights.

These were used to train the model on the cifar100, just trained for 1 epoch to verify and then evaluated on the testset

What I found is weird when the batch size is 12 the model gives a test accuracy of 20% (which I found unsettling since I was using the pretrained weights)

I then test on batch size of …

computervision found impact implementation performance pytorch train verify