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Pedestrian Attribute Recognition On Pa 100K

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
strongBaseline(ours)78.56Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method-
PATH (Partial FT)90.8HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining-
HP-net72.19%HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis-
UniHCP (finetune)86.18UniHCP: A Unified Model for Human-Centric Perceptions-
Label2Label79.23Label2Label: A Language Modeling Framework for Multi-Attribute Learning-
APTM80.17Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark-
Hulk(Finetune, ViT-B)87.85Hulk: A Universal Knowledge Translator for Human-Centric Tasks-
Hulk(Finetune, ViT-L)88.97Hulk: A Universal Knowledge Translator for Human-Centric Tasks-
Attribute-Specific Localization77.08%Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization-
C2T-Net87.2C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition
SOLIDER86.38Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks-
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Pedestrian Attribute Recognition On Pa 100K | SOTA | HyperAI