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Pedestrian Detection On Citypersons

评估指标

Heavy MR^-2
Reasonable MR^-2
Small MR^-2
Test Time

评测结果

各个模型在此基准测试上的表现结果

Paper TitleRepository
TLL53.615.5--Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation-
FRCNN-15.425.6-CityPersons: A Diverse Dataset for Pedestrian Detection
FRCNN+Seg-14.822.6-CityPersons: A Diverse Dataset for Pedestrian Detection
TLL+MRF52.014.4--Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation-
RepLoss56.913.2--Repulsion Loss: Detecting Pedestrians in a Crowd
OR-CNN55.712.8--Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd-
ALFNet51.912.019.00.27Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting-
CSP (with offset) + ResNet-5049.311.016.00.33s/imgCenter and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
NOH-NMS53.010.8--NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
FRCNN+FPN-Res50+refined feature map+Crowdhuman-10.67--CrowdHuman: A Benchmark for Detecting Human in a Crowd
Beta R-CNN47.110.6--Beta R-CNN: Looking into Pedestrian Detection from Another Perspective-
NMS-Loss-10.08--NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
SOLIDER39.49.7--Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
VLPD43.19.410.9-VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
ACSP46.39.3--Adapted Center and Scale Prediction: More Stable and More Accurate
F2DNet32.68.711.30.44s/imgF2DNet: Fast Focal Detection Network for Pedestrian Detection
LSFM31.98.58.80.18Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving-
F2DNet (extra data)26.237.89.430.44s/imgF2DNet: Fast Focal Detection Network for Pedestrian Detection
Pedestron33.97.58.0-Generalizable Pedestrian Detection: The Elephant In The Room
LSFM (Additional Data)24.736.387.900.18Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving-
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