hiennguyen9874 / person-attribute-recognition

Person attribute recognition

Person Attribute Recognition

Install

• pip3 install -r requirements.txt

Dataset

• Manual download from github.com/dangweili/pedestrian-attribute-recognition-pytorch and extract dataset.
  • PETA

    data_dir/
    |--peta/
    |  |--images/
    |  |  |--00001.png
    |  |  |--00002.png
    |  |  |--...
    |  |--PETA.mat
    

  • PA-100K

    data_dir/
    |--pa_100k/
    |  |--images/
    |  |  |--0000001.png
    |  |  |--0000002.png
    |  |  |--...
    |  |--annotation.mat
    

    data_dir in config file.

Run

• python3 train.py --config <path/to/config_file.yml>

Config

• Using config file in config folder.
• Add new config file based on config/base_epoch.yml or config/base_episode.yml.

Result

Peta dataset

backbone	bn after linear	Head	Loss	mA	Accuracy	Precision	Recall	F1-Score
resnet50	✓	BNHead	CEL_Sigmoid	84.79	80.07	88.28	86.24	86.98
resnet50	✓	BNHead	BCEWithLogitsLoss	79.47	76.33	87.22	82.38	84.33
resnet50_ibn_a_nl	✓	BNHead	CEL_Sigmoid	83.49	79.60	88.89	85.14	86.65
osnet	✓	ReductionHead	CEL_Sigmoid	77.67	73.44	84.17	80.60	81.97
osnet	✓	ReductionHead	BCEWithLogitsLoss	71.00	67.49	85.60	72.94	77.94
osnet	✓	BNHead	CEL_Sigmoid	77.89	72.57	83.68	79.96	81.32
resnet50		BNHead	CEL_Sigmoid	82.67	78.61	88.53	84.17	85.91
resnet50_ibn_a_nl		BNHead	CEL_Sigmoid	82.24	78.57	88.48	84.20	85.91
osnet		ReductionHead	CEL_Sigmoid	77.93	73.00	83.82	80.65	81.81
osnet		BNHead	CEL_Sigmoid	77.72	73.04	84.65	79.82	81.68

PA-100K

backbone	bn after linear	Head	Loss	mA	Accuracy	Precision	Recall	F1-Score
resnet50	✓	BNHead	CEL_Sigmoid	79.50	78.89	88.17	86.28	86.80

Deploy model with torchserve

Acknowledgements

• Rethinking of Pedestrian Attribute Recognition: Realistic Datasets and A Strong Baseline
• Bag of Tricks and A Strong Baseline for Deep Person Re-identification
• Omni-Scale Feature Learning for Person Re-Identification
• PyTorch Template Project by victoresque
• Torchreid

person-attribute-recognition's Issues

trainer.py

你好，在trainer.py 导包的时候from losses import build_losses，我好像没有看到build_losses，可以帮忙看看吗，谢谢

FileNotFoundError: [Errno 2] No such file or directory: 'base/config.json'

FileNotFoundError: [Errno 2] No such file or directory: 'base/config.json'

about attention

Please, can you share a little bit about how to use your attention code? Are there any configurations we should use?

What is the full name of PPE dataset?

how do we make it work along with object detector?

is it possible to use this model as the 2nd stage of a 2 stage object detector?

where is the paper?

Hello,Do you have a corresponding paper？

Peta dataset is only 137 bytes when downloaed using your code

how to do inference?