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Open Images 2019 - Object Detection

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Best sub leaderboard

What if every single team had chosen their best sub for final scoring?
This LB is based on that scenario.
Team name Public(final) Private(final) Private(best) Public score Private score Date
MMfruit ○○○○○ 1 1 1 0.6817434985447439 0.6588794353161422 2019-10-01
imagesearch ○○○○○○○ 2 2 2 0.6815507331268243 0.6533768152740322 2019-10-01
Prisms ○○○○○○ 3 3 3 0.6717006993073649 0.6421400431005828 2019-10-01
PFDet ○○○○○○ 4 4 4 0.6545419910916067 0.6222128895998085 2019-10-01
Omni-Detection ○○○ 7 5 5 0.6314055993197135 0.6040615299663721 2019-10-01
Schwert 8 6 6 0.6279883712167365 0.602313094303076 2019-10-01
Team 5 ○○○○○ 6 7 7 0.6321830994466183 0.6021069017689084 2019-10-01
pudae 5 8 8 0.6317208574687069 0.5975146316745799 2019-09-29
[ods.ai] n01z3 9 9 9 0.6263424171046708 0.5953512920787639 2019-10-01
dingwoai 12 10 10 0.6083479969748391 0.5850483846048451 2019-09-30
J ○○○○○ 10 11 11 0.6211436739905456 0.5825979409442268 2019-10-01
Markup Oracles ○○ 11 12 12 0.613285678518789 0.5730484091161372 2019-10-01
ang TSS method 13 13 13 0.581169428405522 0.5435269016921465 2019-09-30
tito 14 14 14 0.5746578634299946 0.5388591260219696 2019-09-26
Appian 15 15 15 0.5709064705168942 0.5362945000984799 2019-09-07
Noah ○○○○○ 16 16 16 0.5575438473344205 0.5302843645953553 2019-10-01
Wiwide 20 17 17 0.5518182548647067 0.521910253123596 2019-09-30
VisualTagging-CVC-UKOBE ○○○○○ 18 18 18 0.5525092918917801 0.5218715950147478 2019-10-01
onlyone ○○ 25 19 19 0.5426713832311524 0.5195143947673608 2019-08-29
Objectively detecting ○○○○ 17 22 20 0.5566244337314427 0.517378572586176 2019-10-01
Wisers ○○ 22 21 21 0.5491813284202955 0.5173142441059628 2019-09-30
Quatro ○○○○ 21 23 22 0.5494386457507788 0.5143430842783824 2019-09-26
[ods.ai] 512 TPUs ○○ 19 24 23 0.5534711236291694 0.5047163938089266 2019-09-30
T Nakamura (2GPUs) 24 25 24 0.5426736018005994 0.5038749574624034 2019-10-01