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README

A Simple Visual-Textual Baseline for Pedestrian Attribute Recognition

🔧Requirements

Installation

pip install -r requirements.txt

Data Preparation

cd dataset/preprocess
python rap.py

Pre-trained Model

ImageNet pre-trained ViT-Base need to be download for training.

🚀Training

python train.py RAP

📌Citation

If you found this code/work to be useful in your own research, please consider citing the following:

@article{cheng2022simple,
  title={A Simple Visual-Textual Baseline for Pedestrian Attribute Recognition},
  author={Cheng, Xinhua and Jia, Mengxi and Wang, Qian and Zhang, Jian},
  journal={IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)},
  year={2022}
}

👍Acknowledgements

This code is based on Rethinking_of_PAR and TransReID. Thanks for their efforts.

Core symbols most depended-on inside this repo

_cfg
called by 13
models/vit.py
append
called by 8
tools/function.py
time_str
called by 4
tools/utils.py
state_dict
called by 4
solver/scheduler.py
update_groups
called by 4
solver/scheduler.py
update
called by 3
tools/utils.py
trunc_normal_
called by 3
models/vit.py
to_scalar
called by 2
tools/utils.py

Shape

Method 61
Function 43
Class 19

Languages

Python100%

Modules by API surface

tools/utils.py45 symbols
models/vit.py29 symbols
solver/scheduler.py10 symbols
tools/function.py6 symbols
solver/cosine_lr.py6 symbols
dataset/AttrDataset.py5 symbols
solver/lr_scheduler.py3 symbols
models/base_block.py3 symbols
loss/CE_loss.py3 symbols
dataset/preprocess/rap.py3 symbols
dataset/preprocess/pa100k.py3 symbols
train.py2 symbols

For agents

$ claude mcp add VTB \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact