Home News Latest Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Crowd Counting On Worldexpo10

Metrics

Average MAE

Results

Performance results of various models on this benchmark

Model Name	Average MAE	Paper Title	Repository
IG-CNN	11.3	Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN	-
SANet	8.2	Scale Aggregation Network for Accurate and Efficient Crowd Counting
SPANet	7.7	Learning Spatial Awareness to Improve Crowd Counting	-
MCNN	11.6	Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
DecideNet	9.23	DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation	-
CAN	7.4	Context-Aware Crowd Counting	-
Switch-CNN	9.4	Switching Convolutional Neural Network for Crowd Counting	-
M-SFANet	7.32	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting	-
CP-CNN	8.9	Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs	-
ACSCP	7.5	Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
Zhang et al.	12.9	Cross-Scene Crowd Counting via Deep Convolutional Neural Networks	-
D-ConvNet	9.1	Crowd Counting With Deep Negative Correlation Learning	-
CSRNet	8.6	CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes	-
ECAN	7.2	Context-Aware Crowd Counting	-
ic-CNN	10.3	Iterative Crowd Counting	-

0 of 15 row(s) selected.

Crowd Counting On Worldexpo10 | SOTA | HyperAI