https://github.com/ajithvcoder/pedestrian_attribute_recognition
https://github.com/ajithvcoder/pedestrian_attribute_recognition
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/ajithvcoder/pedestrian_attribute_recognition
- Owner: ajithvcoder
- Created: 2020-07-21T10:41:35.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-03T21:59:32.000Z (about 2 years ago)
- Last Synced: 2025-04-08T19:48:12.719Z (9 months ago)
- Language: Python
- Size: 2 MB
- Stars: 9
- Watchers: 1
- Forks: 3
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
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README
# Pedestrian_Attribute_Recognition
This repo is heavily borowed from https://github.com/valencebond/Strong_Baseline_of_Pedestrian_Attribute_Recognition
Paper [Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method](https://arxiv.org/abs/2005.11909).
## 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.
### SOTA Performance
- 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.
## 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/)]
## Zero-shot Protocal
Realistic datasets of PETA and RAPv2 are provided at [Google Drive](https://drive.google.com/drive/folders/1vPtWyJ1Qjf0T6t3zPLi4EzXCMZ46Clqg?usp=sharing).
You can just replace the 'dataset.pkl' with 'peta_new.pkl' or 'rapv2_new.pkl' to run experiments under new protocal.
## Pretrained Models
Pretrained models are provided now at [Google Drive](https://drive.google.com/drive/folders/1t2SG7-jAalF8gx3uvApA6hUzVh_lR-y0?usp=sharing).
Because we ran the experiments again, so there may be subtle differences in performance.
## 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
```
3. 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/
```
4. 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
```
5. Train baseline based on resnet50
```
CUDA_VISIBLE_DEVICES=0 python train.py PETA
```
## Acknowledgements
Codes are based on the repository from [Dangwei Li](https://github.com/dangweili/pedestrian-attribute-recognition-pytorch)
and [Houjing Huang](https://github.com/dangweili/pedestrian-attribute-recognition-pytorch). Thanks for their released code.
### Citation
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}
}