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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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