Pedestrian Attribute Recognition On Peta
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| HP-net | 76.13% | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | |
| Attribute-Specific Localization | 79.52% | Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization | |
| strongbaseline | 79.14% | Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method | |
| ALM[tang2019Improving] (ICCV19) | 79.52% | Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method | |
| UniHCP (FT) | 88.78% | UniHCP: A Unified Model for Human-Centric Perceptions | |
| C2T-Net | 88.20% | C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition |
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