The original weights for those DenseNet models were trained in Torch with bicubic interpolation, so use bicubic interpolation in the Scale transform. The default in PyTorch is generally bilinear.
Also, accuracies of pretrained model don’t necessarily stay static over time. With changes in various parts of the system (cudnn, PyTorch mechanics, OpenCV/Pillow image processing, other libraries) you can see small changes over time, whether or not it’s statistically significant is another matter.
I benchmarked this model 1.5 years ago with whatever was the lastest versions of all dependencies and got > 77. Now it’s just under.
1.5 years ago w/ bicubic: 77.290 (22.710), 93.478 (6.522)
now w/ bicubic:  76.950 (23.050), 93.410 (6.590)
now w/ bilinear:  76.594 (23.406), 93.258 (6.742)