Torchreid-Pip: Deep learning person re-identification in PyTorch.
Project description
Torchreid-Pip: Packaged version of Torchreid
This repo is a packaged version of the Torchreid algorithm.
Installation
pip install torchreid
Overview
1. Import torchreid
import torchreid
2. Load data manager
datamanager = torchreid.data.ImageDataManager(
root="reid-data",
sources="market1501",
targets="market1501",
height=256,
width=128,
batch_size_train=32,
batch_size_test=100,
transforms=["random_flip", "random_crop"]
)
3 Build model, optimizer and lr_scheduler
model = torchreid.models.build_model(
name="resnet50",
num_classes=datamanager.num_train_pids,
loss="softmax",
pretrained=True
)
model = model.cuda()
optimizer = torchreid.optim.build_optimizer(
model,
optim="adam",
lr=0.0003
)
scheduler = torchreid.optim.build_lr_scheduler(
optimizer,
lr_scheduler="single_step",
stepsize=20
)
4. Build engine
engine = torchreid.engine.ImageSoftmaxEngine(
datamanager,
model,
optimizer=optimizer,
scheduler=scheduler,
label_smooth=True
)
5. Run training and test
engine.run(
save_dir="log/resnet50",
max_epoch=60,
eval_freq=10,
print_freq=10,
test_only=False
)
Citation
If you use this code or the models in your research, please give credit to the following papers:
@article{torchreid,
title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch},
author={Zhou, Kaiyang and Xiang, Tao},
journal={arXiv preprint arXiv:1910.10093},
year={2019}
}
@inproceedings{zhou2019osnet,
title={Omni-Scale Feature Learning for Person Re-Identification},
author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
booktitle={ICCV},
year={2019}
}
@article{zhou2021osnet,
title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
journal={TPAMI},
year={2021}
}
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