# Strong_Baseline_of_Pedestrian_Attribute_Recognition

**Repository Path**: wang-tf/Strong_Baseline_of_Pedestrian_Attribute_Recognition

## Basic Information

- **Project Name**: Strong_Baseline_of_Pedestrian_Attribute_Recognition
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-20
- **Last Updated**: 2021-10-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## A Strong Baseline of Pedestrian Attribute Recognition

Considering the big performance gap of various SOTA baseline, we provide a solid and strong baseline for fair comparison.



## Dependencies

- pytorch 1.4.0
- torchvision 0.5.0
- tqdm 4.43.0
- easydict 1.9


## Tricks
- 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


## Performance Comparision

### Baseline Performance

- 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](https://github.com/cvcode18/imbalanced_learning), [VAC](https://github.com/hguosc/visual_attention_consistency) and thanks to their code.
- We also try our best to reimplement ALM and try to contact the authors, but no reply received.

![BaselinePerf](https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition/blob/master/imgs/baseline.png)


![BaselinePerf](https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition/blob/master/imgs/baseline_rap2.png)


### SOTA Performance

- Compared with performance of recent state-of-the-art methods, the performance of our baseline is comparable, even better.

![SOTAPerf](https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition/blob/master/imgs/SOTA.png)


- 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.


## Dataset Info
PETA: Pedestrian Attribute Recognition At Far Distance [[Paper](http://mmlab.ie.cuhk.edu.hk/projects/PETA_files/Pedestrian%20Attribute%20Recognition%20At%20Far%20Distance.pdf)][[Project](http://mmlab.ie.cuhk.edu.hk/projects/PETA.html)]

PA100K[[Paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.pdf)][[Github](https://github.com/xh-liu/HydraPlus-Net)]

RAP : A Richly Annotated Dataset for Pedestrian Attribute Recognition 
- v1.0 [[Paper](https://arxiv.org/pdf/1603.07054v3.pdf)][[Project](http://www.rapdataset.com/)]
- v2.0 [[Paper](https://ieeexplore.ieee.org/abstract/document/8510891)][[Project](http://www.rapdataset.com/)]


## Get Started
1. Run `git clone https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition.git`
2. 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 generate `dataset.pkl` respectively
    ```
    python ./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
    ``` 
 
## Reference

- https://github.com/dangweili/pedestrian-attribute-recognition-pytorch
- https://github.com/huanghoujing/EANet