OMG! Almost 2 weeks and more than 70 submissions to try to understand why CV/LB gap was so huge. My first assumption was that I messed up the labels mapping on my side but I did not take time to fully probe LB to detect this issue. Round1 baseline notebook here is just wrong for round2.
class_labels = {
“aegypti”: 0,
“albopictus”: 1,
“anopheles”: 2,
“culex”: 3,
“culiseta”: 4,
“japonicus/koreicus”: 5
}
One has to notice the correct labels in random model here:
self.class_names = [
‘culex’,
'japonicus-koreicus’,
‘culiseta’,
‘albopictus’,
‘anopheles’,
‘aegypti’
]
Without your finding @saidinesh_pola I was on the way to conclude that test set distribution was totally different than train set and I was going to give up the lottery.
Thanks again! The competition can start now.
BTW: The submission issue I’ve reported is still here. It happens around 2 on 10 submissions for me. Same code submitted twice either fail or succeed. The 2 second limit check per image is not stable. Even, I think it’s not 2 seconds but more 1.2 second limit to make a submission work safely. So, one should try to submit the same more times to make sure it works/fails really.