Torchreid is a library built on PyTorch for deep-learning person re-identification.
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 of ranking results
Documentation: https://kaiyangzhou.github.io/deep-person-reid/.
24-03-2019: Torchreid documentation is out!
- Install PyTorch and torchvision following the official instructions.
- Install Cython.
- Clone
deep-person-reidto your preferred directory.
$ git clone https://github.com/KaiyangZhou/deep-person-reid.git- Install
torchreidvia
$ cd deep-person-reid/
$ python setup.py install # or python3
$ # If you wanna modify the source code without
$ # the need to re-build it, you can do
$ # python setup.py develop- Import
torchreid
import torchreid- Load data manager
datamanager = torchreid.data.ImageDataManager(
root='reid-data',
sources='market1501',
height=256,
width=128,
batch_size=32,
market1501_500k=False
)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 including a default parser file default_parser.py and the main script main.py. For example, to train an image reid model on Market1501 using softmax, you can do
python main.py \
--root path/to/reid-data \
--app image \
--loss softmax \
--label-smooth \
-s market1501 \
-a resnet50 \
--optim adam \
--lr 0.0003 \
--max-epoch 60 \
--stepsize 20 40 \
--batch-size 32 \
--save-dir log/resnet50-market-softmax \
--gpu-devices 0Please refer to default_parser.py and main.py for more details.
Please link this project in your paper