Crowd Counting On Shanghaitech A

Métriques

MAE

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	MAE	Paper Title	Repository
SANet	67.0	Scale Aggregation Network for Accurate and Efficient Crowd Counting
Cascaded-MTL	101.3	CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
OrdinalEntropy	65.6	Improving Deep Regression with Ordinal Entropy
SGANet + CL	57.6	Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
CAN	62.3	Context-Aware Crowd Counting
GauNet (ResNet-50)	54.8	Rethinking Spatial Invariance of Convolutional Networks for Object Counting
CSRNet-EBC	66.3	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
M-SFANet+M-SegNet	57.55	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
S-DCNet	58.3	From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer	-
CSRNet	68.2	CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
IG-CNN	72.5	Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN	-
D-ConvNet	73.5	Crowd Counting With Deep Negative Correlation Learning	-
CP-CNN	73.6	Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs	-
CLIP-EBC (ViT-B/16)	52.5	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
DMCount-EBC	62.3	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
LoViTCrowd	54.8	Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting
Liu et al.	73.6	Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
CLIP-EBC (ResNet50)	54.0	CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
PSL-Net	49.9	Crowd Counting and Individual Localization Using Pseudo Square Label	-
Zhang et al.	181.8	Cross-Scene Crowd Counting via Deep Convolutional Neural Networks	-

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