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Object Counting On Carpk

Métriques

MAE

RMSE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MAE	RMSE	Paper Title	Repository
YOLO (2016)	156.00	57.55	You Only Look Once: Unified, Real-Time Object Detection	-
Faster R-CNN (2015)	39.88	47.67	Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks	-
One-Look Regression (2016)	21.88	36.73	A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning	-
Soft-IoU + EM-Merger unit	6.77	8.52	Precise Detection in Densely Packed Scenes	-
LPN Counting (2017)	22.76	34.46	Drone-based Object Counting by Spatially Regularized Regional Proposal Network	-
HLCNN	2.12	3.02	An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks
VLCounter	6.46	8.68	VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting	-
CounTR	5.75	7.45	CounTR: Transformer-based Generalised Visual Counting	-
SAFECount	5.33	7.04	Few-shot Object Counting with Similarity-Aware Feature Enhancement	-
YOLO9000opt (2017)	130.40	172.46	YOLO9000: Better, Faster, Stronger	-
CounTX (uses arbitrary text input to specify object to count, used "the cars" for CARPK)	8.13	10.87	Open-world Text-specified Object Counting	-
RetinaNet (2018)	16.62	22.30	Drone-based Object Counting by Spatially Regularized Regional Proposal Network	-
RetinaNet (2018)	24.58	-	Focal Loss for Dense Object Detection	-
BMNet+	5.76	7.83	Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting	-

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Object Counting On Carpk | SOTA | HyperAI