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BAcc@10 over time

Each dot is a team improving its own best public BAcc@10 score; the stepped line follows the best score overall. Hover a dot for the team.

team improves its own bestnew best overallstate of the art

0.765

Best BalAcc@10

Teams

3,154

Submissions

—

Days remaining

Metric:

Teams by BalAcc@10

Top-10 balanced accuracy on the 50-word vocab — Kaggle’s official public score

1Munich Logic Lab

0.7651

2Kim

0.7475

3dav0dea

0.7444

4Brain & AI

0.7399

5Varshith Madishetty

0.7372

6Stéphane d'Ascoli

0.7309

7MindLink

0.7305

8Artem Blanar

0.7303

9Jatin Arutla

0.7298

10Jordan Griffith

0.7288

11noah

0.7273

12ottietjesesakfs

0.7180

13titus fisher

0.7173

14snute220905

0.7171

15Umur Yıldız

0.7148

16Connor Finnerty

0.7062

17BNEL

0.7028

18Michal

0.7003

19cfw21w2

0.6984

20linkP

0.6966

Showing top 20 of 76 teams — full standings in the table below.

Best score per team — every metric

Kaggle ranks one number. We archive every submission and re-score it ourselves, so each team's best on each metric sits side by side (public split). Click a metric to rank by it.

#	Team	BalAcc@10 ↓	BalAcc@1	OVMI	Subs
1	Munich Logic Lab	0.7651	0.2772	1.2085	85
2	Kim	0.7475	0.2721	1.1469	151
3	dav0dea	0.7444	0.2915	1.2233	148
4	Brain & AI	0.7399	0.3389	1.2079	36
5	Varshith Madishetty	0.7372	0.3071	1.1479	122
6	Stéphane d'Ascoli	0.7309	0.3062	1.1302	5
7	MindLink	0.7305	0.2539	1.1265	243
8	Artem Blanar	0.7303	0.2577	1.1661	85
9	Jatin Arutla	0.7298	0.3048	1.1630	101
10	Jordan Griffith	0.7288	0.3096	1.1960	158
11	noah	0.7273	0.2479	1.0873	8
12	ottietjesesakfs	0.7180	0.2587	1.1591	302
13	titus fisher	0.7173	0.2680	1.1617	110
14	snute220905	0.7171	0.3068	1.1544	11
15	Umur Yıldız	0.7148	0.2432	1.1690	110
16	Connor Finnerty	0.7062	0.2661	1.1639	107
17	BNEL	0.7028	0.2998	1.1866	49
18	Michal	0.7003	0.2814	1.1495	54
19	cfw21w2	0.6984	0.2433	1.1607	71
20	linkP	0.6966	0.2734	1.1495	38
21	NeuroMTEC	0.6965	0.2140	1.1388	15
22	Infera-Neuro	0.6876	0.2157	1.1848	75
23	Karahan Yılmazer	0.6809	0.2024	1.1509	73
24	peperonata	0.6649	0.2489	1.1567	21
25	saharacamel	0.6639	0.2242	1.1181	51
26	Nova	0.6620	0.2290	1.1888	17
27	CWBZ	0.6601	0.2089	1.1239	33
28	Dimitar Terziev	0.6572	0.2438	1.0764	53
29	JovanaLab	0.6558	0.2729	1.0757	26
30	MagnetoLex	0.6548	0.2333	1.1227	50
31	Yahya Abdulselam	0.6544	0.1998	1.0499	15
32	Viacheslav Fokin	0.6528	0.1631	1.1206	17
33	kakuteki	0.6463	0.2239	1.1474	47
34	neural2speech	0.6447	0.1447	1.1265	10
35	Brain King	0.6441	0.1522	1.1299	71
36	MindReaders2.0	0.6431	0.1853	1.1673	161
37	Picu	0.6428	0.2249	1.1318	2
38	@Arnauya	0.6428	0.1899	1.1423	81
39	langstudent	0.6328	0.2457	1.0446	24
40	FengYuXiang 123	0.6316	0.2290	1.0614	25
41	albedoweb	0.6314	0.1481	0.9553	3
42	SENPAI	0.6296	0.1858	1.0606	61
43	onlytry	0.6152	0.1934	1.0748	9
44	Ave_Dominus_Nox	0.5880	0.1761	1.0726	23
45	OmarDotEmam	0.5838	0.1797	1.0068	7
46	syouya tobita	0.5658	0.1210	1.1454	18
47	Oysta A.H	0.5587	0.1074	1.0193	17
48	bcs	0.5378	0.0943	0.9860	11
49	NJUersgogogo	0.4500	0.0716	0.9557	26
50	Baseline	0.4444	0.0733	0.9803	1
51	kongxuan47	0.4394	0.0717	0.9022	3
52	Sia	0.4362	0.1014	0.9362	5
53	Salsinats	0.4032	0.0693	0.8101	19
54	Abhijith Pradeep	0.3672	0.0641	0.8567	5
55	Shuntaro Suzuki	0.3540	0.0488	0.8104	5
56	liuyishj	0.3343	0.0490	0.7152	9
57	Jun Jie Li	0.3176	0.0864	0.8436	12
58	monte-carlo	0.2900	0.0483	0.4671	5
59	Bhargav Kowshik	0.2871	0.0292	1.0947	16
60	yu_kuo14	0.2772	0.0517	0.8975	2
61	弗洛洛	0.2755	—	—	2
62	Fon1as	0.2538	0.0249	0.2581	1
63	willguido	0.2444	0.0244	0.1239	4
64	Akbar Permana	0.2422	0.0260	0.5392	2
65	immortal2	0.2407	0.0140	0.9038	1
66	Amal Messaoud	0.2374	0.0316	0.5402	9
67	Srinivas Calindi	0.2175	0.0373	0.2497	4
68	Nguyen Anh	0.2164	0.0311	0.5181	3
69	ML Learner26	0.2000	0.0200	0.0000	1
70	Akshat	0.2000	0.0200	0.0000	1
71	DongGua	0.2000	0.0200	0.0000	1
72	Athina Kanellatou	0.2000	0.0200	0.0000	1
73	Shriya Patkar	0.1950	0.0245	0.1998	1
74	lavender_lover	0.1906	0.0200	0.4893	3
75	LizaSheina	0.1880	0.0284	0.3681	1
76	Viknesh .V	0.1600	0.0200	0.0000	1

About these numbers

BalAcc@10 is Kaggle's official public score, shown here exactly as Kaggle reports it. BalAcc@1 (strict top-1) and OVMI (the information a decoder recovers over the vocabulary) are computed by us from the submitted files, on the same public split — final placements use the private split, revealed after the deadline.

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