Pedestrian Detection On Citypersons
Metrics
Heavy MR^-2
Reasonable MR^-2
Small MR^-2
Test Time
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Heavy MR^-2 | Reasonable MR^-2 | Small MR^-2 | Test Time |
|---|---|---|---|---|
| f2dnet-fast-focal-detection-network-for | 26.23 | 7.8 | 9.43 | 0.44s/img |
| pedestrian-detection-the-elephant-in-the-room | 33.9 | 7.5 | 8.0 | - |
| citypersons-a-diverse-dataset-for-pedestrian | - | 14.8 | 22.6 | - |
| small-scale-pedestrian-detection-based-on | 52.0 | 14.4 | - | - |
| citypersons-a-diverse-dataset-for-pedestrian | - | 15.4 | 25.6 | - |
| small-scale-pedestrian-detection-based-on | 53.6 | 15.5 | - | - |
| localized-semantic-feature-mixers-for | 31.9 | 8.5 | 8.8 | 0.18 |
| adapted-center-and-scale-prediction-more | 42.5 | - | - | - |
| beta-r-cnn-looking-into-pedestrian-detection-1 | 47.1 | 10.6 | - | - |
| nms-loss-learning-with-non-maximum | - | 10.08 | - | - |
| increasing-pedestrian-detection-performance | 28.37 | 6.23 | 7.36 | - |
| repulsion-loss-detecting-pedestrians-in-a | 56.9 | 13.2 | - | - |
| crowdhuman-a-benchmark-for-detecting-human-in | - | 10.67 | - | - |
| f2dnet-fast-focal-detection-network-for | 32.6 | 8.7 | 11.3 | 0.44s/img |
| adapted-center-and-scale-prediction-more | 46.3 | 9.3 | - | - |
| localized-semantic-feature-mixers-for | 24.73 | 6.38 | 7.90 | 0.18 |
| beyond-appearance-a-semantic-controllable | 39.4 | 9.7 | - | - |
| high-level-semantic-feature-detectiona-new | 49.3 | 11.0 | 16.0 | 0.33s/img |
| vlpd-context-aware-pedestrian-detection-via | 43.1 | 9.4 | 10.9 | - |
| learning-efficient-single-stage-pedestrian | 51.9 | 12.0 | 19.0 | 0.27 |
| occlusion-aware-r-cnn-detecting-pedestrians | 55.7 | 12.8 | - | - |
| noh-nms-improving-pedestrian-detection-by | 53.0 | 10.8 | - | - |