Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting (official Pytorch implementation)
This paper submitted to TIP is the extension of the previous Arxiv paper.
This project is adopted in the JDAI-CV/fast-reid and PP-Human of PaddleDetection.
- provide a strong baseline for Pedestrian Attribute Recognition and Multi-Label Classification.
- provide two new datasets RAPzs and PETAzs following zero-shot pedestrian identity setting.
- provide a general training pipeline for pedestrian attribute recognition and multi-label classification task.
- DDP training, which is mainly used for multi-label classifition.
- Training on all attributes, testing on "selected" attribute. Because the proportion of positive samples for other attributes is less than a threshold, such as 0.01.
- For PETA and PETAzs, 35 of the 105 attributes are selected for performance evaluation.
- For RAPv1, 51 of the 92 attributes are selected for performance evaluation.
- For RAPv2 and RAPzs, 54 and 53 of the 152 attributes are selected for performance evaluation.
- For PA100k, all attributes are selected for performance evaluation.
- However, training on all attributes can not bring consistent performance improvement on various datasets.
- EMA model.
- Transformer-base model, such as swin-transformer (with a huge performance improvement) and vit.
- Convenient dataset info file like dataset_all.pkl
-
PETA: Pedestrian Attribute Recognition At Far Distance [Paper][Project]
-
RAP : A Richly Annotated Dataset for Pedestrian Attribute Recognition
-
PETAzs & RAPzs : Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting Paper [Project]
| Datasets | Models | ma | Acc | Prec | Rec | F1 |
|---|---|---|---|---|---|---|
| PA100k | resnet50 | 80.21 | 79.15 | 87.79 | 87.01 | 87.40 |
| -- | resnet50* | 79.85 | 79.13 | 89.45 | 85.40 | 87.38 |
| -- | resnet50 + EMA | 81.97 | 80.20 | 88.06 | 88.17 | 88.11 |
| -- | bninception | 79.13 | 78.19 | 87.42 | 86.21 | 86.81 |
| -- | TresnetM | 74.46 | 68.72 | 79.82 | 80.71 | 80.26 |
| -- | swin_s | 82.19 | 80.35 | 87.85 | 88.51 | 88.18 |
| -- | vit_s | 79.40 | 77.61 | 86.41 | 86.22 | 86.32 |
| -- | vit_b | 81.01 | 79.38 | 87.60 | 87.49 | 87.55 |
| PETA | resnet50 | 83.96 | 78.65 | 87.08 | 85.62 | 86.35 |
| PETAzs | resnet50 | 71.43 | 58.69 | 74.41 | 69.82 | 72.04 |
| RAPv1 | resnet50 | 79.27 | 67.98 | 80.19 | 79.71 | 79.95 |
| RAPv2 | resnet50 | 78.52 | 66.09 | 77.20 | 80.23 | 78.68 |
| RAPzs | resnet50 | 71.76 | 64.83 | 78.75 | 76.60 | 77.66 |
- The resnet* model is trained by using the weighted function proposed by Tan in AAAI2020.
- Performance in PETAzs and RAPzs based on the first version of PETAzs and RAPzs as described in paper.
- Experiments are conducted on the input size of (256, 192), so there may be minor differences from the results in the paper.
- The reported performance can be achieved at the first drop of learning rate. We also take this model as the best model.
- Pretrained models are provided now at Google Drive.
| Datasets | Models | mAP | CP | CR | CF1 | OP | OR | OF1 |
|---|---|---|---|---|---|---|---|---|
| COCO | resnet101 | 82.75 | 84.17 | 72.07 | 77.65 | 85.16 | 75.47 | 80.02 |
- python 3.7
- pytorch 1.7.0
- torchvision 0.8.2
- cuda 10.1
- Run
git clone https://github.com/valencebond/Rethinking_of_PAR.git - Create a directory to dowload above datasets.
cd Rethinking_of_PAR mkdir data - Prepare datasets to have following structure:
${project_dir}/data PETA images/ PETA.mat dataset_all.pkl dataset_zs_run0.pkl PA100k data/ dataset_all.pkl RAP RAP_dataset/ RAP_annotation/ dataset_all.pkl RAP2 RAP_dataset/ RAP_annotation/ dataset_zs_run0.pkl COCO14 train2014/ val2014/ ml_anno/ category.json coco14_train_anno.pkl coco14_val_anno.pkl - Train baseline based on resnet50
sh train.sh
Codes are based on the repository from Dangwei Li and Houjing Huang. Thanks for their released code.
If you use this method or this code in your research, please cite as:
@article{jia2021rethinking,
title={Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting},
author={Jia, Jian and Huang, Houjing and Chen, Xiaotang and Huang, Kaiqi},
journal={arXiv preprint arXiv:2107.03576},
year={2021}
}
@inproceedings{jia2021spatial,
title={Spatial and semantic consistency regularizations for pedestrian attribute recognition},
author={Jia, Jian and Chen, Xiaotang and Huang, Kaiqi},
booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
pages={962--971},
year={2021}
}
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