  Accurately predicting the performance of architecture with small sample
training is an important but not easy task. How to analysis and train dataset
to overcome overfitting is the core problem we should deal with. Meanwhile if
there is the mult-task problem, we should also think about if we can take
advantage of their correlation and estimate as fast as we can. In this track,
Super Network builds a search space based on ViT-Base. The search space contain
depth, num-heads, mpl-ratio and embed-dim. What we done firstly are
pre-processing the data based on our understanding of this problem which can
reduce complexity of problem and probability of over fitting. Then we tried
different kind of models and different way to combine them. Finally we choose
stacking ensemble models using GP-NAS with cross validation. Our stacking model
ranked 1st in CVPR 2022 Track 2 Challenge.
