Torchreid is a library for deep-learning person re-identification in PyTorch.
It features:
- multi-GPU training
- support both image- and video-reid
- end-to-end training and evaluation
- incredibly easy preparation of reid datasets
- multi-dataset training
- cross-dataset evaluation
- standard protocol used by most research papers
- highly extensible (easy to add models, datasets, training methods, etc.)
- implementations of state-of-the-art deep reid models
- access to pretrained reid models
- advanced training techniques
- visualization tools (tensorboard, ranks, etc.)
Code: https://github.com/KaiyangZhou/deep-person-reid.
Documentation: https://kaiyangzhou.github.io/deep-person-reid/.
How-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide.
Model zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.
Tech report: https://arxiv.org/abs/1910.10093.
You can find some research projects that are built on top of Torchreid here.
- [Nov 19]
ImageDataManagercan load training data from target datasets by settingload_train_targets=True, and the train-loader can be accessed withtrain_loader_t = datamanager.train_loader_t. This feature is useful for domain adaptation research.
Make sure conda is installed.
# cd to your preferred directory and clone this repo
git clone https://github.com/KaiyangZhou/deep-person-reid.git
# create environment
cd deep-person-reid/
conda create --name torchreid python=3.7
conda activate torchreid
# install dependencies
# make sure `which python` and `which pip` point to the correct path
pip install -r requirements.txt
# install torch and torchvision (select the proper cuda version to suit your machine)
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
# install torchreid (don't need to re-build it if you modify the source code)
python setup.py develop- Import
torchreid
import torchreid- 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
)- Build engine
engine = torchreid.engine.ImageSoftmaxEngine(
datamanager,
model,
optimizer=optimizer,
scheduler=scheduler,
label_smooth=True
)- Run training and test
engine.run(
save_dir='log/resnet50',
max_epoch=60,
eval_freq=10,
print_freq=10,
test_only=False
)In "deep-person-reid/scripts/", we provide a unified interface to train and test a model. See "scripts/main.py" and "scripts/default_config.py" for more details. "configs/" contains some predefined configs which you can use as a starting point.
Below we provide an example to train and test OSNet (Zhou et al. ICCV'19). Assume PATH_TO_DATA is the directory containing reid datasets.
To train OSNet on Market1501, do
python scripts/main.py \
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
--transforms random_flip random_erase \
--root $PATH_TO_DATA \
--gpu-devices 0The config file sets Market1501 as the default dataset. If you wanna use DukeMTMC-reID, do
python scripts/main.py \
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
-s dukemtmcreid \
-t dukemtmcreid \
--transforms random_flip random_erase \
--root $PATH_TO_DATA \
--gpu-devices 0 \
data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelrThe code will automatically (download and) load the ImageNet pretrained weights. After the training is done, the model will be saved as "log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250". Under the same folder, you can find the tensorboard file. To visualize the learning curves using tensorboard, you can run tensorboard --logdir=log/osnet_x1_0_market1501_softmax_cosinelr in the terminal and visit http://localhost:6006/ in your web browser.
Evaluation is automatically performed at the end of training. To run the test again using the trained model, do
python scripts/main.py \
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
--root $PATH_TO_DATA \
--gpu-devices 0 \
model.load_weights log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250 \
test.evaluate TrueSuppose you wanna train OSNet on DukeMTMC-reID and test its performance on Market1501, you can do
python scripts/main.py \
--config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml \
-s dukemtmcreid \
-t market1501 \
--transforms random_flip color_jitter \
--root $PATH_TO_DATA \
--gpu-devices 0Here we only test the cross-domain performance. However, if you also want to test the performance on the source dataset, i.e. DukeMTMC-reID, you can set -t dukemtmcreid market1501, which will evaluate the model on the two datasets separately.
Different from the same-domain setting, here we replace random_erase with color_jitter. This can improve the generalization performance on the unseen target dataset.
Pretrained models are available in the Model Zoo.
If you find this code useful to your research, please cite 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{zhou2019learning,
title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
journal={arXiv preprint arXiv:1910.06827},
year={2019}
}