then you've evaluated on the validation set over your method or over your data set okay so you take your data set you split it into two the training and the test set and as long as it does well on the test set you're fine right and the test set comes from the same distribution as the training set so it's the same statistics what we have here is that we want to test our algorithms on something that's not really in the in the test set of the of the data set that's something else so we train on say detecting cars from from imagenet data