Implementation details of hyper-parameters in CfgSearch and Cfg

[JackyWang2001]

I am following your work and running the code on VGD, with pertained features from dataset_setup.md But I failed to get the results mentioned in the paper: my test accuracy was often 2%~5% lower. Could you possibly provide more experimental details about the hyper-parameters such as CfgSearch and Cfg (e.g. ALPHA_START, ALPHA_EVERY, ALPHA_WEIGHT_DECAY, NET_OPTIM_WARMUP, NET_LR_DECAY_R), and other potentially helpful tricks?
Thanks for your preeminent work and help.

[ParadoxZW]

Which architecture do you use (the one we provided or searched by yourself)? arch and default hyper-parameters should produce accuracy reported.

[JackyWang2001]

Thanks for your replies.
I just re-ran the original code to ensure. Run MMNAS for VGD on RefCOCO+ testA by

python search_vgd.py

python train_vgd.py --RUN='train' --GPU='0, 1' --BS=600 --DATASET='refcoco+' --ARCH_PATH='./arch/train_vgd-search.json' --GENO_EPOCH=99

python train_vgd.py --RUN='test' --GPU='0, 1' --DATASET='refcoco+' --CKPT_PATH='./logs/ckpts/train_vgd-full_epoch13.pkl' --ARCH_PATH='./arch/train_vgd-search.json' --GENO_EPOCH=99 --VERSION="16Nov_refcoco+"

I got the validation accuracy of 69.325% and test accuracy of 75.585%. The paper reports the accuracy to be above 80%. Is there anything wrong with my usage?