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
比较表格
| 模型名称 | MAE |
|---|---|
| cnn-based-cascaded-multi-task-learning-of | 252 |
| deep-residual-learning-for-image-recognition | 190 |
| context-aware-crowd-counting | 107 |
| switching-convolutional-neural-network-for | 228 |
| clip-ebc-clip-can-count-accurately-through | 80.5 |
| rethinking-spatial-invariance-of-1 | 81.6 |
| composition-loss-for-counting-density-map | 132 |
| encoder-decoder-based-convolutional-neural | 85.6 |
| improving-point-based-crowd-counting-and | 80.1 |
| segnet-a-deep-convolutional-encoder-decoder | 270 |
| clip-ebc-clip-can-count-accurately-through | 79.3 |
| segmentation-guided-attention-network-for | 87.6 |
| crowd-counting-and-individual-localization | 85.5 |
| clip-ebc-clip-can-count-accurately-through | 76.06 |
| densely-connected-convolutional-networks | 163 |
| segmentation-guided-attention-network-for | 89.1 |
| multi-source-multi-scale-counting-in | 315 |
| single-image-crowd-counting-via-multi-column-1 | 277 |
| clip-ebc-clip-can-count-accurately-through | 77.2 |
| clip-ebc-clip-can-count-accurately-through | 75.90 |
| distribution-matching-for-crowd-counting | 85.6 |
| clip-ebc-clip-can-count-accurately-through | 80.3 |