Crowd Counting On Shanghaitech B
評価指標
MAE
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | MAE |
|---|---|
| iterative-crowd-counting | 10.7 |
| cnn-based-cascaded-multi-task-learning-of | 20 |
| clip-ebc-clip-can-count-accurately-through | 6.6 |
| rethinking-spatial-invariance-of-1 | 6.0 |
| iterative-correlation-based-feature | 9.98 |
| leveraging-unlabeled-data-for-crowd-counting | 13.7 |
| clip-ebc-clip-can-count-accurately-through | 6.0 |
| improving-point-based-crowd-counting-and | 8.7 |
| switching-convolutional-neural-network-for | 21.6 |
| csrnet-dilated-convolutional-neural-networks | 10.6 |
| improving-deep-regression-with-ordinal | 9.1 |
| from-open-set-to-closed-set-counting-objects | 6.7 |
| encoder-decoder-based-convolutional-neural | 6.32 |
| cross-scene-crowd-counting-via-deep | 32.0 |
| clip-ebc-clip-can-count-accurately-through | 6.9 |
| clip-ebc-clip-can-count-accurately-through | 7.0 |
| fusioncount-efficient-crowd-counting-via | 6.9 |
| distribution-matching-for-crowd-counting | 7.4 |
| divide-and-grow-capturing-huge-diversity-in | 13.6 |
| single-image-crowd-counting-via-multi-column-1 | 26.4 |
| segmentation-guided-attention-network-for | 6.6 |
| learning-spatial-awareness-to-improve-crowd | 6.5 |
| generating-high-quality-crowd-density-maps | 20.1 |
| clip-ebc-clip-can-count-accurately-through | 5.9 |
| locate-size-and-count-accurately-resolving | 8.1 |
| context-aware-crowd-counting | 7.8 |
| crowd-counting-via-adversarial-cross-scale | 17.2 |
| crowd-counting-and-individual-localization | 5.8 |
| scale-aggregation-network-for-accurate-and | 8.4 |
| crowd-counting-with-deep-negative-correlation | 18.7 |
| segmentation-guided-attention-network-for | 6.3 |