The code for our paper Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method Considering the big performance gap of various SOTA baseline, we provide a solid and strong baseline for fair comparison.
- pytorch 1.4.0
- torchvision 0.5.0
- tqdm 4.43.0
- easydict 1.9
- sample-wise loss not label-wise loss
- big learning rate combined with clip_grad_norm
- augmentation Pad combined with RandomCrop
- add BN after classifier layer
- Compared with baseline performance of MsVAA, VAC, ALM, our baseline make a huge performance improvement.
- Compared with our reimplementation of MsVAA, VAC, ALM, our baseline is better.
- We try our best to reimplement MsVAA, VAC and thanks to their code.
- We also try our best to reimplement ALM and try to contact the authors, but no reply received.
- Compared with performance of recent state-of-the-art methods, the performance of our baseline is comparable, even better.
- DeepMAR (ACPR15) Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios.
- HPNet (ICCV17) Hydraplus-net: Attentive deep features for pedestrian analysis.
- JRL (ICCV17) Attribute recognition by joint recurrent learning of context and correlation.
- LGNet (BMVC18) Localization guided learning for pedestrian attribute recognition.
- PGDM (ICME18) Pose guided deep model for pedestrian attribute recognition in surveillance scenarios.
- GRL (IJCAI18) Grouping Attribute Recognition for Pedestrian with Joint Recurrent Learning.
- RA (AAAI19) Recurrent attention model for pedestrian attribute recognition.
- VSGR (AAAI19) Visual-semantic graph reasoning for pedestrian attribute recognition.
- VRKD (IJCAI19) Pedestrian Attribute Recognition by Joint Visual-semantic Reasoning and Knowledge Distillation.
- AAP (IJCAI19) Attribute aware pooling for pedestrian attribute recognition.
- MsVAA (ECCV18) Deep imbalanced attribute classification using visual attention aggregation.
- VAC (CVPR19) Visual attention consistency under image transforms for multi-label image classification.
- ALM (ICCV19) Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization.
PETA: Pedestrian Attribute Recognition At Far Distance [Paper][Project]
RAP : A Richly Annotated Dataset for Pedestrian Attribute Recognition
- Run
git clone https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition.git
- Create a directory to dowload above datasets.
cd Strong_Baseline_of_Pedestrian_Attribute_Recognition mkdir data
- Prepare datasets to have following structure:
${project_dir}/data PETA images/ PETA.mat README PA100k data/ annotation.mat README.txt RAP RAP_dataset/ RAP_annotation/ RAP2 RAP_dataset/ RAP_annotation/
- Run the
format_xxxx.py
to generatedataset.pkl
respectivelypython ./dataset/preprocess/format_peta.py python ./dataset/preprocess/format_pa100k.py python ./dataset/preprocess/format_rap.py python ./dataset/preprocess/format_rap2.py
- Train baseline based on resnet50
CUDA_VISIBLE_DEVICES=0 python train.py PETA
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:
@misc{jia2020rethinking,
title={Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method},
author={Jian Jia and Houjing Huang and Wenjie Yang and Xiaotang Chen and Kaiqi Huang},
year={2020},
eprint={2005.11909},
archivePrefix={arXiv},
primaryClass={cs.CV}
}