Low training accuracy using pre-trained torchvision model

I am trying to evaluate a pre-trained mobilenetv2 model from torchvision on the ImageNet training dataset using the official example (examples/main.py at master · pytorch/examples · GitHub).
To do so, I modify lines 235-237 to perform validation on the train loader instead of the val loader:
if args.evaluate: validate(train_loader, model, criterion, args) return
Everything else is left untouched. The command I use to run is:
python imagenet_train_example.py -a mobilenet_v2 -j 16 -b 1024 -e --pretrained /data/ImageNet
However, the results are much lower than expected:

Acc@1 2.926 Acc@5 15.079 Loss 11.795791

I was wondering if anyone knows why that might be? Am I doing something wrong?