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Object Counting On Carpk

Metrics

MAE
RMSE

Results

Performance results of various models on this benchmark

Model Name
MAE
RMSE
Paper TitleRepository
YOLO (2016)156.0057.55You Only Look Once: Unified, Real-Time Object Detection
Faster R-CNN (2015)39.8847.67Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
One-Look Regression (2016)21.8836.73A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning-
Soft-IoU + EM-Merger unit6.778.52Precise Detection in Densely Packed Scenes
LPN Counting (2017)22.7634.46Drone-based Object Counting by Spatially Regularized Regional Proposal Network-
HLCNN2.123.02An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks
VLCounter6.468.68VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting
CounTR5.757.45CounTR: Transformer-based Generalised Visual Counting
SAFECount5.337.04Few-shot Object Counting with Similarity-Aware Feature Enhancement
YOLO9000opt (2017)130.40172.46YOLO9000: Better, Faster, Stronger
CounTX (uses arbitrary text input to specify object to count, used "the cars" for CARPK)8.1310.87Open-world Text-specified Object Counting
RetinaNet (2018)16.6222.30Drone-based Object Counting by Spatially Regularized Regional Proposal Network-
RetinaNet (2018)24.58-Focal Loss for Dense Object Detection
BMNet+5.767.83Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
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