Object Counting On Coco Count Test
Metrics
m-reIRMSE
m-reIRMSE-nz
mRMSE
mRMSE-nz
Results
Performance results of various models on this benchmark
Model Name | m-reIRMSE | m-reIRMSE-nz | mRMSE | mRMSE-nz | Paper Title | Repository |
|---|---|---|---|---|---|---|
| glance-ft-2L | 0.23 | 0.91 | 0.42 | 2.25 | Counting Everyday Objects in Everyday Scenes | - |
| Fast-RCNN | 0.20 | 1.13 | 0.49 | 2.78 | Counting Everyday Objects in Everyday Scenes | - |
| Supervised Density Map | 0.18 | 0.84 | 0.34 | 1.89 | Object Counting and Instance Segmentation with Image-level Supervision | - |
| LC-ResFCN | 0.19 | 0.99 | 0.38 | 2.20 | Where are the Blobs: Counting by Localization with Point Supervision | - |
| Seq-sub-ft-3x3 | 0.18 | 0.82 | 0.35 | 1.96 | Counting Everyday Objects in Everyday Scenes | - |
| ens | 0.18 | 0.81 | 0.36 | 1.98 | Counting Everyday Objects in Everyday Scenes | - |
| Aso-sub-ft-3x3 | 0.24 | 0.87 | 0.38 | 2.08 | Counting Everyday Objects in Everyday Scenes | - |
0 of 7 row(s) selected.