问题
I want to evaluate my model using K-Fold Cross Validation (k=5). This means that dataset must be split in 5 parts: p1,p2,p3,p4,p5 and then:
(run1) Test: p1,p2,p3,p4 Eval: p5
(run2) Test: p1,p2,p3,p4 Eval: p4
(run3) Test: p1,p2,p4,p5 Eval: p3
(run4) Test: p1,p3,p4,p5 Eval: p2
(run5) Test: p2,p3,p4,p5 Eval: p1
At the end, I calculate the average mean among all the evaluations.
This is essentially K-Fold Cross validation. Right now, what I am doing is to regenerate .tf records each time and then to run the evaluation phase for each one of these combinations. Is there a way to automatize all this procedure? Please note that there is no specific code. In fact, each time I regenerate the tf records. This is what should be automatized as well.
来源：https://stackoverflow.com/questions/51989259/k-fold-cross-validation-tensorflow-object-detection