Crowd Counting On Ucf Qnrf

Métriques

MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MAE	Paper Title	Repository
Cascaded-MTL	252	CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting	-
Resnet101	190	Deep Residual Learning for Image Recognition	-
CAN	107	Context-Aware Crowd Counting	-
Switch-CNN	228	Switching Convolutional Neural Network for Crowd Counting	-
CLIP-EBC (ResNet50)	80.5	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification	-
GauNet (ResNet-50)	81.6	Rethinking Spatial Invariance of Convolutional Networks for Object Counting	-
Idrees et al.	132	Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds	-
M-SFANet	85.6	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting	-
APGCC	80.1	Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance	-
Encoder-Decoder	270	SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation	-
CSRNet-EBC	79.3	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification	-
SGANet + CL	87.6	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss	-
PSL-Net	85.5	Crowd Counting and Individual Localization Using Pseudo Square Label	-
DMCount-EBC (32, dynamic)	76.06	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification	-
Densenet201	163	Densely Connected Convolutional Networks	-
SGANet	89.1	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss	-
Idrees et al.	315	Multi-source Multi-scale Counting in Extremely Dense Crowd Images	-
MCNN	277	Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
DMCount-EBC	77.2	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification	-
DMCount-EBC (16, dynamic)	75.90	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification	-

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