Leaderboard
Rank |
Institution |
Model name |
Date |
Paper |
FPS |
Hardware |
Meta |
Other |
AP |
AP50 |
AP75 |
AR1 |
AR10 |
1 |
PanGu |
PanGu CV |
23-Apr-2023
|
|
15 |
A100 |
|
✔ |
0.5597 |
0.9134 |
0.5830 |
0.1287 |
0.5875 |
2 |
USYD |
object detection |
25-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5583 |
0.9100 |
0.5780 |
0.1302 |
0.5864 |
3 |
USYD |
object detection |
25-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5537 |
0.9070 |
0.5724 |
0.1300 |
0.5828 |
4 |
USYD |
object detection |
24-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5517 |
0.9066 |
0.5638 |
0.1304 |
0.5796 |
5 |
|
aaa |
06-Apr-2023
|
|
10 |
V100 |
|
|
0.5490 |
0.9105 |
0.5546 |
0.1299 |
0.5756 |
6 |
Fraunhofer IOSB |
VFNet |
14-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5484 |
0.9062 |
0.5548 |
0.1287 |
0.5745 |
7 |
Fraunhofer IOSB |
DetectoRS |
13-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5439 |
0.8988 |
0.5509 |
0.1281 |
0.5739 |
8 |
Fraunhofer IOSB |
VFNet |
14-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5424 |
0.9030 |
0.5469 |
0.1277 |
0.5727 |
9 |
Fraunhofer IOSB |
VFNet |
20-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5410 |
0.9016 |
0.5399 |
0.1272 |
0.5711 |
10 |
BUPT MCPRL |
yolo |
26-Sep-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5409 |
0.9053 |
0.5484 |
0.1277 |
0.5728 |
11 |
Fraunhofer IOSB |
DetectoRS |
20-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5386 |
0.8982 |
0.5409 |
0.1279 |
0.5701 |
12 |
Fraunhofer IOSB |
DetectoRS |
13-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5379 |
0.8959 |
0.5383 |
0.1291 |
0.5697 |
13 |
Fraunhofer IOSB |
DetectoRS |
24-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5379 |
0.8983 |
0.5361 |
0.1281 |
0.5702 |
14 |
Fraunhofer IOSB |
DetectoRS |
23-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5371 |
0.8970 |
0.5387 |
0.1278 |
0.5694 |
15 |
Fraunhofer IOSB |
DetectoRS |
10-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5356 |
0.8951 |
0.5361 |
0.1290 |
0.5691 |
16 |
Fraunhofer IOSB |
DetectoRS |
23-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5329 |
0.8974 |
0.5377 |
0.1276 |
0.5666 |
17 |
BUPT MCPRL |
yolo test |
22-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5298 |
0.9021 |
0.5322 |
0.1241 |
0.5682 |
18 |
Fraunhofer IOSB |
DetectoRS |
10-Oct-2022
|
|
1 |
Tesla V100 |
|
|
0.5277 |
0.8914 |
0.5234 |
0.1281 |
0.5631 |
19 |
BUPT MCPRL |
yolov |
25-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5268 |
0.8993 |
0.5264 |
0.1239 |
0.5782 |
20 |
HSU IMB SmartShip |
DyHead |
25-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5224 |
0.8987 |
0.5225 |
0.1254 |
0.5669 |
21 |
BUPT MCPRL |
yolo |
25-Oct-2022
|
|
1 |
Tesla V100 |
|
✔ |
0.5204 |
0.8947 |
0.5183 |
0.1231 |
0.5746 |
22 |
HSU IMB SmartShip |
DyHead |
23-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5114 |
0.8921 |
0.4962 |
0.1247 |
0.5599 |
23 |
HSU IMB SmartShip |
SmartShip |
23-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5109 |
0.8879 |
0.4967 |
0.1253 |
0.5613 |
24 |
HSU IMB SmartShip |
DyHead |
23-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5084 |
0.8905 |
0.4847 |
0.1237 |
0.5548 |
25 |
|
zzz |
04-Apr-2023
|
|
10 |
gpu |
|
|
0.5046 |
0.8724 |
0.4918 |
0.1273 |
0.5406 |
26 |
HSU IMB SmartShip |
DyHead |
24-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.5028 |
0.8966 |
0.4763 |
0.1251 |
0.5501 |
27 |
University of Seoul |
yolo |
14-Oct-2022
|
|
15 |
RTX 3090 |
|
✔ |
0.4995 |
0.8776 |
0.4876 |
0.1251 |
0.5429 |
28 |
Unica |
YoloCNSv4.1 |
11-Oct-2022
|
|
60 |
TeslaP6 |
|
✔ |
0.4915 |
0.8638 |
0.4817 |
0.1245 |
0.5372 |
29 |
FIT BUT |
YOLOv7 |
21-Oct-2022
|
|
6 |
RTX3090 |
|
✔ |
0.4915 |
0.8660 |
0.4778 |
0.1247 |
0.5346 |
30 |
University of Seoul |
yolo |
17-Oct-2022
|
|
24 |
RTX 3090 |
|
✔ |
0.4907 |
0.8663 |
0.4737 |
0.1240 |
0.5371 |
31 |
Granular AI |
YOLOv7 |
19-Oct-2022
|
|
10 |
RTX 3090 |
|
|
0.4880 |
0.8664 |
0.4710 |
0.1227 |
0.5300 |
32 |
Granular AI |
YOLOv7 |
17-Oct-2022
|
|
10 |
RTX 3090 |
|
|
0.4876 |
0.8680 |
0.4675 |
0.1241 |
0.5323 |
33 |
HSU IMB SmartShip |
DyHead |
25-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.4876 |
0.8750 |
0.4679 |
0.1222 |
0.5462 |
34 |
Unica |
YoloCNSv4 |
11-Oct-2022
|
|
60 |
TeslaP6 |
|
✔ |
0.4873 |
0.8527 |
0.4751 |
0.1244 |
0.5337 |
35 |
Unica |
YoloCNSv3 |
30-Sep-2022
|
|
60 |
TeslaP6 |
|
✔ |
0.4849 |
0.8524 |
0.4727 |
0.1226 |
0.5313 |
36 |
Granular AI |
YOLOv7 |
19-Oct-2022
|
|
10 |
RTX 3090 |
|
|
0.4832 |
0.8621 |
0.4642 |
0.1227 |
0.5269 |
37 |
Granular AI |
YOLOv7 |
25-Oct-2022
|
|
80 |
RTX 3090 |
|
|
0.4806 |
0.8523 |
0.4597 |
0.1241 |
0.5269 |
38 |
Granular AI |
YOLOv7 |
17-Oct-2022
|
|
10 |
RTX 3090 |
|
|
0.4805 |
0.8609 |
0.4566 |
0.1234 |
0.5218 |
39 |
TUK |
M8 |
21-Oct-2022
|
|
33 |
RTX3090 |
|
✔ |
0.4798 |
0.8566 |
0.4562 |
0.1249 |
0.5243 |
40 |
HKUST |
HKUST VGD V |
25-Oct-2022
|
|
10 |
3090 |
|
|
0.4792 |
0.8764 |
0.4599 |
0.1155 |
0.5205 |
41 |
Granular AI |
YOLOv7 |
18-Oct-2022
|
|
10 |
RTX 3090 |
|
|
0.4786 |
0.8670 |
0.4570 |
0.1195 |
0.5179 |
42 |
HKUST |
HKUST VGD V |
25-Oct-2022
|
|
10 |
3090 |
|
|
0.4755 |
0.8694 |
0.4532 |
0.1170 |
0.5160 |
43 |
|
Y7 |
25-Oct-2022
|
|
100 |
RTX 3070 |
|
|
0.4754 |
0.8634 |
0.4537 |
0.1181 |
0.5234 |
44 |
TUK |
M8 |
20-Oct-2022
|
|
33 |
RTX3090 |
|
✔ |
0.4750 |
0.8443 |
0.4557 |
0.1243 |
0.5205 |
45 |
Granular AI |
YOLOv7 |
25-Oct-2022
|
|
80 |
RTX 3090 |
|
|
0.4745 |
0.8509 |
0.4560 |
0.1186 |
0.5243 |
46 |
TUK |
M5 |
14-Oct-2022
|
|
33 |
RTX3090 |
|
✔ |
0.4743 |
0.8350 |
0.4641 |
0.1218 |
0.5218 |
47 |
Durham University |
DurObj |
20-Oct-2022
|
|
4 |
TITAN XP |
|
|
0.4733 |
0.8563 |
0.4389 |
0.1226 |
0.5174 |
48 |
PiVa AI |
TBD |
12-Oct-2022
|
🔗
|
-1 |
TBD |
|
|
0.4716 |
0.8582 |
0.4455 |
0.1206 |
0.5095 |
49 |
TUK |
M9 |
23-Oct-2022
|
|
33 |
RTX3090 |
|
|
0.4675 |
0.8361 |
0.4376 |
0.1235 |
0.5118 |
50 |
TUK |
M10 |
24-Oct-2022
|
|
1 |
RTX3090 |
|
|
0.4653 |
0.8342 |
0.4414 |
0.1239 |
0.5096 |
51 |
TUK |
M7 |
21-Oct-2022
|
|
10 |
RTX3090 |
|
|
0.4646 |
0.8322 |
0.4382 |
0.1229 |
0.5127 |
52 |
TUK |
M7 |
22-Oct-2022
|
|
1 |
RTX3090 |
|
|
0.4627 |
0.8325 |
0.4342 |
0.1236 |
0.5093 |
53 |
NYU |
YOLO |
23-Oct-2022
|
|
1 |
2080 |
|
✔ |
0.4624 |
0.8268 |
0.4439 |
0.1222 |
0.5175 |
54 |
ISTI |
VFNet64 |
30-May-2023
|
|
1 |
RTX 4090 |
|
✔ |
0.4616 |
0.8439 |
0.4306 |
0.1231 |
0.5086 |
55 |
PiVa AI |
TBD |
12-Oct-2022
|
🔗
|
-1 |
TBD |
|
|
0.4614 |
0.8511 |
0.4274 |
0.1206 |
0.5033 |
56 |
|
yolo |
19-Oct-2022
|
|
-1 |
2080 |
|
|
0.4600 |
0.8300 |
0.4424 |
0.1203 |
0.5161 |
57 |
Granular AI |
YOLOv7 |
24-Oct-2022
|
|
80 |
RTX 3070 |
|
|
0.4586 |
0.8294 |
0.4419 |
0.1161 |
0.5131 |
58 |
PiVa AI |
TBD |
09-Oct-2022
|
🔗
|
-1 |
TBD |
|
|
0.4563 |
0.8522 |
0.4209 |
0.1194 |
0.4989 |
59 |
|
YoloCNSv2 |
24-Sep-2022
|
|
60 |
TeslaP6 |
|
✔ |
0.4554 |
0.8389 |
0.4376 |
0.1165 |
0.5093 |
60 |
TUK |
M9 |
23-Oct-2022
|
|
5 |
RTX3090 |
|
|
0.4552 |
0.8628 |
0.4073 |
0.1218 |
0.5101 |
61 |
|
RISC |
17-Oct-2022
|
|
1 |
2080 |
|
✔ |
0.4520 |
0.8295 |
0.4249 |
0.1199 |
0.5093 |
62 |
KLE Tech CEVI |
Object Detection |
13-Oct-2022
|
|
60 |
RTX 3050 |
|
✔ |
0.4503 |
0.8475 |
0.4117 |
0.1214 |
0.4951 |
63 |
KLE Tech CEVI |
Object Detection |
13-Oct-2022
|
|
60 |
RTX 3050 |
|
✔ |
0.4503 |
0.8475 |
0.4117 |
0.1214 |
0.4951 |
64 |
KLE Tech CEVI |
Object Detection |
10-Oct-2022
|
|
60 |
Tesla P100 |
|
|
0.4503 |
0.8475 |
0.4117 |
0.1214 |
0.4951 |
65 |
FIT BUT |
TOOD |
21-Oct-2022
|
|
1 |
RTX3090 |
|
✔ |
0.4486 |
0.8147 |
0.4197 |
0.1209 |
0.4985 |
66 |
KLE Tech CEVI |
Object Detection |
13-Oct-2022
|
|
60 |
RTX 3050 |
|
|
0.4482 |
0.8480 |
0.4086 |
0.1212 |
0.4932 |
67 |
Santa Clara University |
Object Detection |
21-Oct-2022
|
|
3 |
Nvidia Titan |
|
|
0.4414 |
0.8265 |
0.4146 |
0.1144 |
0.4913 |
68 |
HSU IMB SmartShip |
DyHead |
24-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.4367 |
0.8284 |
0.3936 |
0.1170 |
0.5045 |
69 |
12 |
belele |
24-May-2023
|
|
123 |
123 |
|
|
0.4338 |
0.8262 |
0.3959 |
0.1168 |
0.4857 |
70 |
asd |
lala |
24-May-2023
|
|
1 |
312 |
✔ |
|
0.4282 |
0.8174 |
0.3896 |
0.1150 |
0.4771 |
71 |
HSU IMB SmartShip |
DyHead |
25-Oct-2022
|
|
1 |
A100 |
|
✔ |
0.4246 |
0.6952 |
0.4428 |
0.1219 |
0.5111 |
72 |
University Tübingen |
Binary OD Baseline |
13-Sep-2022
|
|
66 |
RTX 3080 |
|
✔ |
0.4227 |
0.8177 |
0.3857 |
0.1162 |
0.4749 |
73 |
FEUP |
Adapt 8 5e3 |
29-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
✔ |
|
0.4106 |
0.7909 |
0.3695 |
0.1153 |
0.4650 |
74 |
FEUP |
Adapt 8 1e3 |
26-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
✔ |
|
0.4105 |
0.7941 |
0.3753 |
0.1159 |
0.4658 |
75 |
FEUP |
Adapt 8 1e2 |
29-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
✔ |
|
0.4092 |
0.7915 |
0.3731 |
0.1149 |
0.4621 |
76 |
Santa Clara University |
Object Detection |
07-Oct-2022
|
|
3 |
Titan Xp |
|
|
0.4070 |
0.7979 |
0.3605 |
0.1151 |
0.4577 |
77 |
UESTC |
baseline |
09-Oct-2022
|
|
-1 |
-1 |
|
|
0.4070 |
0.7980 |
0.3604 |
0.1151 |
0.4577 |
78 |
University Tübingen |
Baseline |
12-Sep-2022
|
|
66 |
RTX 3080 |
|
✔ |
0.4070 |
0.7980 |
0.3605 |
0.1151 |
0.4577 |
79 |
FEUP |
Altitude 4 5e3 |
30-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
✔ |
|
0.4068 |
0.7932 |
0.3643 |
0.1136 |
0.4637 |
80 |
FEUP |
epochs |
31-May-2023
|
|
-1 |
NVIDIA 1050 Ti |
|
|
0.4062 |
0.7939 |
0.3715 |
0.1145 |
0.4606 |
81 |
FEUP |
Altitude 4 1e3 |
30-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
✔ |
|
0.4059 |
0.7902 |
0.3635 |
0.1141 |
0.4630 |
82 |
FEUP |
Adapt 4 1e2 |
30-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
✔ |
|
0.4053 |
0.7934 |
0.3604 |
0.1144 |
0.4585 |
83 |
Santa Clara University |
Object Detection |
16-Oct-2022
|
|
3 |
Nvidia Titan |
|
|
0.4050 |
0.7972 |
0.3590 |
0.1144 |
0.4561 |
84 |
FEUP |
both |
01-Jun-2023
|
|
-1 |
NVIDIA 3080 Ti |
|
|
0.4045 |
0.7910 |
0.3603 |
0.1142 |
0.4596 |
85 |
FEUP |
angle 5e3 |
31-May-2023
|
|
-1 |
Nvidia 3080 Ti |
✔ |
|
0.4038 |
0.7872 |
0.3664 |
0.1141 |
0.4615 |
86 |
FEUP |
Baseline - Hyp Evolved |
30-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
|
|
0.4035 |
0.7937 |
0.3579 |
0.1144 |
0.4544 |
87 |
KLE Tech Hubli |
Object Detection |
10-Oct-2022
|
|
30 |
Tesla P100 |
|
|
0.4015 |
0.8076 |
0.3417 |
0.1162 |
0.4496 |
88 |
FEUP |
Baseline - Hyp Evolved |
25-May-2023
|
|
-1 |
NVIDIA 3080 Ti |
|
|
0.4012 |
0.7932 |
0.3556 |
0.1139 |
0.4544 |
89 |
FEUP |
Baseline - Hyp Evolved |
02-Jun-2023
|
|
-1 |
NVIDIA 3080 Ti |
|
✔ |
0.4008 |
0.7846 |
0.3532 |
0.1153 |
0.4520 |
90 |
FEUP |
baseline-8e-lossota |
26-Apr-2023
|
|
1 |
3080 Ti |
|
|
0.3974 |
0.7882 |
0.3581 |
0.1114 |
0.4554 |
91 |
FEUP |
baseline-8e-default |
26-Apr-2023
|
|
1 |
3080 Ti |
|
|
0.3854 |
0.7670 |
0.3365 |
0.1095 |
0.4473 |
92 |
ASD |
altitude nomoisaic |
01-Jun-2023
|
|
-1 |
3080 Ti |
|
|
0.3730 |
0.7592 |
0.3156 |
0.1104 |
0.4320 |
93 |
FEUP |
baseline nomoisaic |
01-Jun-2023
|
|
-1 |
3080 TI |
|
|
0.3687 |
0.7595 |
0.3099 |
0.1087 |
0.4284 |
94 |
FIT BUT |
pix2seq |
21-Oct-2022
|
|
1 |
RTX3090 |
|
✔ |
0.3444 |
0.7700 |
0.2444 |
0.0949 |
0.4656 |
95 |
FIT BUT |
pix2seq |
24-Oct-2022
|
|
1 |
RTX3090 |
|
✔ |
0.3291 |
0.7534 |
0.2234 |
0.0895 |
0.4560 |
96 |
FIT BUT |
DETR |
24-Oct-2022
|
|
1 |
RTX3090 |
|
✔ |
0.3195 |
0.7416 |
0.2260 |
0.1021 |
0.3971 |
97 |
Santa Clara University |
Object Detection |
22-Oct-2022
|
|
3 |
Nvidia Titan |
|
|
0.2816 |
0.5566 |
0.2466 |
0.0945 |
0.3683 |
98 |
Granular AI |
YOLO |
09-Oct-2022
|
|
10 |
RTX 3090 |
|
|
0.2592 |
0.6110 |
0.1765 |
0.0883 |
0.3760 |
99 |
FEUP |
baseline-8e-focalloss |
26-Apr-2023
|
|
1 |
3080 Ti |
|
|
0.2520 |
0.5984 |
0.1689 |
0.0840 |
0.3496 |
100 |
University Tuebingen |
F-RCNN ResNet18 Baseline |
09-Oct-2022
|
|
29 |
GTX 1080 Ti |
|
✔ |
0.2136 |
0.5426 |
0.1343 |
0.0855 |
0.2752 |
101 |
Santa Clara University |
Object Detection |
15-Oct-2022
|
|
3 |
Nvidia Titan |
|
|
0.0972 |
0.1963 |
0.0896 |
0.0630 |
0.1614 |
102 |
Santa Clara University |
Object Detection |
13-Oct-2022
|
|
3 |
Titan Xp |
|
|
0.0964 |
0.1973 |
0.0876 |
0.0627 |
0.1499 |
103 |
pg |
pg |
19-Apr-2023
|
|
15 |
A100 |
|
|
0.0000 |
0.0000 |
0.0000 |
0.0000 |
0.0000 |
104 |
Santa Clara University |
Object Detection |
22-Oct-2022
|
|
3 |
Nvidia Titan |
|
|
0.0000 |
0.0000 |
0.0000 |
0.0000 |
0.0000 |