2D Vehicle Detection on KITTI (test) benchmark leaderboard · SOTA2 Research

2D Vehicle Detection on KITTI (test)

96.4AP (Easy)Deep MANTA

Evaluation Results

Method	Links
Deep MANTA2017.03		96.4	90.1	80.79
Deep MANTA2017.03		95.77	90.03	80.62
3DOP2017.03		93.04	88.64	79.1
Mono3D2017.03		92.33	88.66	78.96
SubCNN2017.03		90.81	89.04	79.27
F-PointNet2018.12		90.78	90	80.8
IPOD2018.12		90.2	89.3	87.37
SDP + RPN2017.03		90.14	88.85	78.38
MS-CNN2017.03		90.03	89.02	76.11
AVOD-FPN2018.12		89.99	87.44	80.05
AVOD2018.12		89.73	88.08	80.14
F-PointNet2018.12		87.81	77.25	74.46
3DVP2017.03		87.46	75.77	65.38
Faster R-CNN2017.03		86.71	81.84	71.12
F-PointNet2018.12		84.9	72.25	65.14
AOG2017.03		84.8	75.94	60.7
Regionlets2017.03		84.75	76.45	59.7
SubCat2017.03		84.14	75.46	59.71
IPOD2018.12		82.9	65.28	57.63
OC-DPM2017.03		75.94	65.95	53.56
DPM-VOC+VP2017.03		74.95	64.71	48.76
IPOD2018.12		73.28	63.07	56.71
MDPM-un-BB2017.03		71.19	62.16	48.43
ACF-SC2017.03		69.11	58.66	45.95
AVOD-FPN2018.12		68.65	59.32	55.82
LSVM-MDPM-sv2017.03		68.2	56.48	44.18
AVOD-FPN2018.12		67.32	58.42	57.44
AVOD2018.12		65.72	56.01	48.89
AVOD2018.12		51.64	43.49	37.79