Deep Object Reid

Deep Object Reid is a library for deep-learning image classification and object re-identification, written in PyTorch. It is a part of OpenVINO™ Training Extensions.

The project is based on Kaiyang Zhou's Torchreid project.

Its 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/openvinotoolkit/deep-object-reid

How-to instructions: https://github.com/openvinotoolkit/deep-object-reid/blob/ote/docs/user_guide.rst

Model zoo by Kaiyang Zhou: https://github.com/openvinotoolkit/deep-object-reid/blob/ote/docs/MODEL_ZOO.md

Original tech report by Kaiyang Zhou and Tao Xiang: https://arxiv.org/abs/1910.10093.

Also you can find some other research projects that are built on top of Torchreid here.

What's new

• [May 2020] Added the person attribute recognition code used in Omni-Scale Feature Learning for Person Re-Identification (ICCV'19). See projects/attribute_recognition/.
• [May 2020] 1.2.1: Added a simple API for feature extraction (torchreid/utils/feature_extractor.py). See the documentation for the instruction.
• [Apr 2020] Code for reproducing the experiments of deep mutual learning in the OSNet paper (Supp. B) has been released at projects/DML.
• [Apr 2020] Upgraded to 1.2.0. The engine class has been made more model-agnostic to improve extensibility. See Engine and ImageSoftmaxEngine for more details. Credit to Dassl.pytorch.
• [Dec 2019] Our OSNet paper has been updated, with additional experiments (in section B of the supplementary) showing some useful techniques for improving OSNet's performance in practice.
• [Nov 2019] ImageDataManager can load training data from target datasets by setting load_train_targets=True, and the train-loader can be accessed with train_loader_t = datamanager.train_loader_t. This feature is useful for domain adaptation research.

Installation

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

Get started: 30 seconds to Torchreid

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
)

A unified interface

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. The folder "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. The environmental variable CUDA_VISIBLE_DEVICES is omitted, which you need to specify if you have a pool of gpus and want to use a specific set of them.

Conventional setting

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

The 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 \
data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelr

The 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 \
model.load_weights log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250 \
test.evaluate True

Cross-domain setting

Suppose 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

Here 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.

Datasets

Image-reid datasets

• Market1501
• CUHK03
• DukeMTMC-reID
• MSMT17
• VIPeR
• GRID
• CUHK01
• SenseReID
• QMUL-iLIDS
• PRID

Video-reid datasets

• MARS
• iLIDS-VID
• PRID2011
• DukeMTMC-VideoReID

Models

ImageNet classification models

• ResNet
• ResNeXt
• SENet
• DenseNet
• Inception-ResNet-V2
• Inception-V4
• Xception
• IBN-Net

Lightweight models

• NASNet
• MobileNetV2
• ShuffleNet
• ShuffleNetV2
• SqueezeNet

ReID-specific models

• MuDeep
• ResNet-mid
• HACNN
• PCB
• MLFN
• OSNet
• OSNet-AIN

Useful links

• OSNet-IBN1-Lite (test-only code with lite docker container)
• Deep Learning for Person Re-identification: A Survey and Outlook

Citation

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}
}