Pedestrian Detection on CityPersons

Metric: Reasonable MR^-2 (higher is better)

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#	Model↕	Reasonable MR^-2▼	Extra Data	Paper	Date↕	Code
1	TLL	15.5	No	Small-scale Pedestrian Detection Based on Somati...	2018-07-04	-
2	FRCNN	15.4	No	CityPersons: A Diverse Dataset for Pedestrian De...	2017-02-19	Code
3	FRCNN+Seg	14.8	No	CityPersons: A Diverse Dataset for Pedestrian De...	2017-02-19	Code
4	TLL+MRF	14.4	No	Small-scale Pedestrian Detection Based on Somati...	2018-07-04	-
5	RepLoss	13.2	No	Repulsion Loss: Detecting Pedestrians in a Crowd	2017-11-21	Code
6	OR-CNN	12.8	No	Occlusion-aware R-CNN: Detecting Pedestrians in ...	2018-07-23	-
7	ALFNet	12	No	-	-	Code
8	CSP (with offset) + ResNet-50	11	No	Center and Scale Prediction: Anchor-free Approac...	2019-04-05	Code
9	NOH-NMS	10.8	No	NOH-NMS: Improving Pedestrian Detection by Nearb...	2020-07-27	Code
10	FRCNN+FPN-Res50+refined feature map+Crowdhuman	10.67	Yes	CrowdHuman: A Benchmark for Detecting Human in a...	2018-04-30	Code
11	Beta R-CNN	10.6	No	Beta R-CNN: Looking into Pedestrian Detection fr...	2022-10-23	-
12	NMS-Loss	10.08	No	NMS-Loss: Learning with Non-Maximum Suppression ...	2021-06-04	Code
13	SOLIDER	9.7	Yes	Beyond Appearance: a Semantic Controllable Self-...	2023-03-30	Code
14	VLPD	9.4	No	VLPD: Context-Aware Pedestrian Detection via Vis...	2023-04-06	Code
15	ACSP	9.3	No	Adapted Center and Scale Prediction: More Stable...	2020-02-20	Code
16	F2DNet	8.7	No	F2DNet: Fast Focal Detection Network for Pedestr...	2022-03-04	Code
17	LSFM	8.5	No	-	-	-
18	F2DNet (extra data)	7.8	Yes	F2DNet: Fast Focal Detection Network for Pedestr...	2022-03-04	Code
19	Pedestron	7.5	Yes	Generalizable Pedestrian Detection: The Elephant...	2020-03-19	Code
20	LSFM (Additional Data)	6.38	Yes	-	-	-
21	DIW Loss	6.23	Yes	-	-	-

#1TLLSOTA
15.5
Reasonable MR^-2· 2018-07-04
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
#2FRCNNSOTA
15.4
Reasonable MR^-2· 2017-02-19
CityPersons: A Diverse Dataset for Pedestrian Detection Code
#3FRCNN+Seg
14.8
Reasonable MR^-2· 2017-02-19
CityPersons: A Diverse Dataset for Pedestrian Detection Code
#4TLL+MRF
14.4
Reasonable MR^-2· 2018-07-04
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
#5RepLoss
13.2
Reasonable MR^-2· 2017-11-21
Repulsion Loss: Detecting Pedestrians in a Crowd Code
#6OR-CNN
12.8
Reasonable MR^-2· 2018-07-23
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
#7ALFNet
12
Reasonable MR^-2
No paperCode
#8CSP (with offset) + ResNet-50
11
Reasonable MR^-2· 2019-04-05
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection Code
#9NOH-NMS
10.8
Reasonable MR^-2· 2020-07-27
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination Code
#10FRCNN+FPN-Res50+refined feature map+Crowdhuman
10.67
Reasonable MR^-2· Extra Data· 2018-04-30
CrowdHuman: A Benchmark for Detecting Human in a Crowd Code
#11Beta R-CNN
10.6
Reasonable MR^-2· 2022-10-23
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
#12NMS-Loss
10.08
Reasonable MR^-2· 2021-06-04
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection Code
#13SOLIDER
9.7
Reasonable MR^-2· Extra Data· 2023-03-30
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks Code
#14VLPD
9.4
Reasonable MR^-2· 2023-04-06
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision Code
#15ACSP
9.3
Reasonable MR^-2· 2020-02-20
Adapted Center and Scale Prediction: More Stable and More Accurate Code
#16F2DNet
8.7
Reasonable MR^-2· 2022-03-04
F2DNet: Fast Focal Detection Network for Pedestrian Detection Code
#17LSFM
8.5
Reasonable MR^-2
No paper
#18F2DNet (extra data)
7.8
Reasonable MR^-2· Extra Data· 2022-03-04
F2DNet: Fast Focal Detection Network for Pedestrian Detection Code
#19Pedestron
7.5
Reasonable MR^-2· Extra Data· 2020-03-19
Generalizable Pedestrian Detection: The Elephant In The Room Code
#20LSFM (Additional Data)
6.38
Reasonable MR^-2· Extra Data
No paper
#21DIW Loss
6.23
Reasonable MR^-2· Extra Data
No paper