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Caltech

아카이브 평가 33건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
LSFM	Reasonable Miss Rate 0.87 Heavy MR^-2 19.5	Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving 2023-01-01	연결 없음
F2DNet (extra data)	Reasonable Miss Rate 1.71 Heavy MR^-2 20.42	F2DNet: Fast Focal Detection Network for Pedestrian Detection 2022-03-04	코드 1 · 코드 2
Pedestron	Reasonable Miss Rate 1.76 Heavy MR^-2 25.7	Generalizable Pedestrian Detection: The Elephant In The Room 2020-03-19	코드 1
F2DNet	Reasonable Miss Rate 2.2 Heavy MR^-2 38.7	F2DNet: Fast Focal Detection Network for Pedestrian Detection 2022-03-04	코드 1 · 코드 2
VLPD	Reasonable Miss Rate 2.3 Heavy MR^-2 37.7	VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision 2023-04-06	코드 1
NMS-Loss	Reasonable Miss Rate 2.92	NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection 2021-06-04	코드 1
FRCNN+FPN-Res50+refined feature map+Crowdhuman	Reasonable Miss Rate 3.46	CrowdHuman: A Benchmark for Detecting Human in a Crowd 2018-04-30	코드 1
CSP + CityPersons dataset	Reasonable Miss Rate 3.8	Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection 2019-04-05	코드 1 · 코드 2
RepLoss + CityPersons dataset	Reasonable Miss Rate 4.0	Repulsion Loss: Detecting Pedestrians in a Crowd 2017-11-21	코드 1 · 코드 2
OR-CNN + CityPersons dataset	Reasonable Miss Rate 4.1	Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd 2018-07-23	연결 없음
CSP	Reasonable Miss Rate 4.5	Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection 2019-04-05	코드 1 · 코드 2
ALFNet + CityPersons dataset	Reasonable Miss Rate 4.5	Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting 2018-09-01	코드 1
RepLoss	Reasonable Miss Rate 5.0	Repulsion Loss: Detecting Pedestrians in a Crowd 2017-11-21	코드 1 · 코드 2
Zhang et al. *	Reasonable Miss Rate 5.1	CityPersons: A Diverse Dataset for Pedestrian Detection 2017-02-19	코드 1 · 코드 2
HyperLearner	Reasonable Miss Rate 5.5	What Can Help Pedestrian Detection? 2017-05-08	연결 없음
Zhang et al.	Reasonable Miss Rate 5.8	CityPersons: A Diverse Dataset for Pedestrian Detection 2017-02-19	코드 1 · 코드 2
ALFNet	Reasonable Miss Rate 6.1	Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting 2018-09-01	코드 1
TFAN	Reasonable Miss Rate 6.5	Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians 2020-06-01	연결 없음
RPN+BF	Reasonable Miss Rate 7.3	Is Faster R-CNN Doing Well for Pedestrian Detection? 2016-07-24	연결 없음
SDS-RCNN	Reasonable Miss Rate 7.36	Illuminating Pedestrians via Simultaneous Detection & Segmentation 2017-06-26	코드 1 · 코드 2
F-DNN+SS	Reasonable Miss Rate 8.18	Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection 2016-10-11	연결 없음
FasterRCNN	Reasonable Miss Rate 8.7	Is Faster R-CNN Doing Well for Pedestrian Detection? 2016-07-24	연결 없음
SA-FastRCNN	Reasonable Miss Rate 9.68	Scale-aware Fast R-CNN for Pedestrian Detection 2015-10-28	연결 없음
MS-CNN	Reasonable Miss Rate 9.95	A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection 2016-07-25	코드 1
MCF	Reasonable Miss Rate 10.40	Learning Multilayer Channel Features for Pedestrian Detection 2016-03-01	연결 없음
CompACT-Deep	Reasonable Miss Rate 11.75	Learning Complexity-Aware Cascades for Deep Pedestrian Detection 2015-07-19	연결 없음
Part-level CNN + saliency and bounding box alignment	Reasonable Miss Rate 12.4	Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment 2018-10-01	코드 1
NNNF	Reasonable Miss Rate 16.20	Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry 2015-11-25	연결 없음
Checkerboards+	Reasonable Miss Rate 17.1	Filtered Channel Features for Pedestrian Detection 2015-01-23	연결 없음
TA-CNN	Reasonable Miss Rate 20.9	Pedestrian Detection aided by Deep Learning Semantic Tasks 2014-11-29	연결 없음