Incremental Learning in Person Re-Identification

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This repository contains code for our research. Paper can be found here, arXiv

Getting started

1. cd ~/PATH_NAME
2. Run git clone https://github.com/prajjwal1/person-reid-incremental
3. Install the specified dependencies, to install use pip3 install - requirements.txt
4. Follow the below mentioned steps for preparation of dataset and performing training

Prerequisites:

• OS: Linux/MacOS
• Pytorch>=0.3

Install Dependencies

• Installing Pytorch

Datasets

• Market 1501
• Duke MTMC

Dataset structure

This is the recommended file structure which was used

For preparation of Market1501

+-- Market1501
|   +-- bounding_box_test
|   +-- bounding_box_train
.......

For preparation of Duke MTMC

+-- dukemtmc-reid
  |   +-- DukeMTMC-reID
    |   +-- bounding_box_test
    |   +-- bounding_box_train
.............

Covariance loss metric has been added to all the modules. You're required to change the flags as per phase as described in paper

Create a directory named as data/ and use the standard directory structure.

For training on Market1501 (Phase 1):

$ python covariance_market1501.py

For training on Duke MTMC (Phase 2).

$ python covariance_duke.py

Make sure to specify the model path properly

SAVED_MODEL_PATH needs to be specified to load the model generated from phase 1

To use ensembling and training, use

$ python covariance_ensembling.py

In this case, you'll have to specify amongst which pipelines do you want to perform ensembling. If you get better results, please file a PR.

To perform training:

While executing make sure to correctly carry out training (Phase 1 and Phase 2) properly as mentioned to achieve incremental learning

When training, log file would be created in the /log directory.

Results:

Takes around 8-9 hours to train the model for 950 epochs (convergence is usually achieved)

Models

We used a ResNet50 along with different architecture of pipelines. We have used hybrid_convnet2. You are required to change the dimensions of the FC layer as per number of classes manually.

To resume training

$ mkdir saved_models

Then specify this as per dir structure in the main module

SAVED_MODEL_PATH = 'saved_models/p1.pth.tar'
checkpoint = torch.load(SAVED_MODEL_PATH)
model.load_state_dict(checkpoint['state_dict'])

For evaluation

$ python evaluation.py

Make sure to set the dataset and path of the models correctly, and also which pipeline to use for evaluation

Citation:

Please cite this, if you use our work

@misc{bhargava2018incremental,
    title={Incremental Learning in Person Re-Identification},
    author={Prajjwal Bhargava},
    year={2018},
    eprint={1808.06281},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}