RAP (Richly Annotated Pedestrian)
Introduced by Li et al. in A Richly Annotated Dataset for Pedestrian Attribute Recognition
The Richly Annotated Pedestrian (RAP) dataset is a dataset for pedestrian attribute recognition. It contains 41,585 images collected from indoor surveillance cameras. Each image is annotated with 72 attributes, while only 51 binary attributes with the positive ratio above 1% are selected for evaluation. There are 33,268 images for the training set and 8,317 for testing.
Source: Localization Guided Learning for Pedestrian Attribute RecognitionBenchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
|---|---|---|---|---|---|
|
RAP
|
Attribute-Specific Localization
|
Papers
| Paper | Code | Results | Date | Stars |
|---|
Dataset Loaders
No data loaders found. You can
submit your data loader here.
No data loaders found. You can
submit your data loader here.
