Why does pre-trained ResNet18 have a higher validation accuracy than training?
For PyTorch's tutorial on performing transfer learning for computer vision (https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html), we can see that there is a higher validation accuracy than training accuracy. Applying the same steps to my own dataset, I see similar results. Why is this the case? Does it have something to do with ResNet 18's architecture?


Assuming there aren't bugs in your code and the train and validation data are in the same domain, then there are a couple reasons why this may occur.


Training loss/acc is computed as the average across an entire training epoch. The network begins the epoch with one set of weights and ends the epoch with a different (hopefully better!) set of weights. During validation you're evaluating everything using only the most recent weights. This means that the comparison between validation and train accuracy is misleading since training accuracy/loss was computed with samples from potentially much worse states of your model. This is usually most noticeable at the start of training or right after the learning rate is adjusted since the network often starts the epoch in a much worse state than it ends. It's also often noticeable when the training data is relatively small (as is the case in your example).
Another difference is the data augmentations used during training that aren't used during validation. During training you randomly crop and flip the training images. While these random augmentations are useful for increasing the ability of your network to generalize they aren't performed during validation because they would diminish performance.


If you were really motivated and didn't mind spending the extra computational power you could get a more meaningful comparison by running the training data back through your network at the end of each epoch using the same data transforms used for validation.
