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Crowd Counting On Ucf Qnrf

评估指标

MAE

评测结果

各个模型在此基准测试上的表现结果

模型名称
MAE
Paper TitleRepository
Cascaded-MTL252CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
Resnet101190Deep Residual Learning for Image Recognition
CAN107Context-Aware Crowd Counting
Switch-CNN228Switching Convolutional Neural Network for Crowd Counting
CLIP-EBC (ResNet50)80.5CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
GauNet (ResNet-50)81.6Rethinking Spatial Invariance of Convolutional Networks for Object Counting
Idrees et al.132Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds-
M-SFANet85.6Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
APGCC80.1Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance
Encoder-Decoder270SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
CSRNet-EBC79.3CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
SGANet + CL87.6Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
PSL-Net85.5Crowd Counting and Individual Localization Using Pseudo Square Label-
DMCount-EBC (32, dynamic)76.06CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
Densenet201163Densely Connected Convolutional Networks
SGANet89.1Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
Idrees et al.315Multi-source Multi-scale Counting in Extremely Dense Crowd Images-
MCNN277Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
DMCount-EBC77.2CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
DMCount-EBC (16, dynamic)75.90CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
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