开源软件名称：

deep-object-reid

开源软件地址：

https://gitee.com/openvinotoolkit-prc/deep-object-reid

开源软件介绍：

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
end-to-end training and evaluation
incredibly easy preparation of reid and classification 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 and lightweight reid/classification models
access to pretrained reid/classification models
advanced training techniques such as mutual learning, RSC, SAM, AugMix and many other
visualization tools (tensorboard, ranks, activation map, etc.)
automated learning rate search and exiting from training (no need to choose epoch number)

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

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

You can find some other research projects that are built on top of Torchreid `here (https://github.com/KaiyangZhou/deep-person-reid/tree/master/projects).

Also if you are planning to perform image classification project, please, refer to OpenVINO™ Training Extensions Custom Image Classification Templates to get a strong baseline for your project. The paper is comming soon.

What's new

[June 2021] Added new algorithms, regularization techniques and models for image classification task
[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. 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 (https://www.anaconda.com/distribution/) is installed.

# cd to your preferred directory and clone this repogit clone https://github.com/openvinotoolkit/deep-object-reid.git# create environmentcd deep-object-reid/conda create --name torchreid python=3.7conda activate torchreid# install dependencies# make sure `which python` and `which pip` point to the correct pathpip install -r requirements.txt# install torchreid (don't need to re-build it if you modify the source code)python setup.py develop

Get started

You can use deep-object-reid in your project or use this repository to train proposed models or your own model through configuration file.

Use deep-object-reid in your project

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'])

Build model, optimizer and lr_scheduler

model = torchreid.models.build_model(    name='osnet_ain_x1_0',    num_classes=datamanager.num_train_pids,    loss='am_softmax',    pretrained=True)model = model.cuda()optimizer = torchreid.optim.build_optimizer(    model,    optim='adam',    lr=0.001)scheduler = torchreid.optim.build_lr_scheduler(    optimizer,    lr_scheduler='single_step',    stepsize=20)

Build engine

engine = torchreid.engine.ImageAMSoftmaxEngine(    datamanager,    model,    optimizer=optimizer,    scheduler=scheduler,    label_smooth=True)

Run training and test

engine.run(    save_dir='log/osnet_ain',    max_epoch=60,    eval_freq=10,    print_freq=10,    test_only=False)

Use deep-object-reid through configuration file

modify one of the following config file and run:

python tools/main.py \--config-file $PATH_TO_CONFIG \--root $PATH_TO_DATA--gpu-num $NUM_GPU

See "tools/main.py" and "scripts/default_config.py" for more details.

Evaluation

Evaluation is automatically performed at the end of training. To run the test again using the trained model, do

python tools/eval.py \--config-file  $PATH_TO_CONFIG\--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 \--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.

Datasets

Image-reid datasets

Classification dataset

Models

Classification models

ReID-specific models

Face Recognition specific models

MobileFaceNet

Useful links

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

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}}