Clothes_Change_Person_ReID

A baseline for clothes-change Person ReID task.

This code is implemented based on JD Fast-reid.

News

• [2020.12.15] LTCC dataset is supported.
• [2020.12.16] Add evaluation code for clothes-change setting.
• [2020.12.16] Train without/with clothes labels.

Todo list

• Add human keypoints
• Add human mask

Installation

See INSTALL.md.

Datasets

The clothes-change datasets include:

• LTCC [dataset] [paper] : 17119 images from 152 identities
• VC-Clothes [dataset] [paper]: 19060 images from 512 identities (Virtual) + 4324 images from 28 identities (Real)
• PRCC [dataset] [paper]: 33698 images from 221 identities

Our experiments are based on LTCC. You can place LTCC dataset to make the data folder like:

${ROOT}
|-- datasets
`-- |-- LTCC_ReID
    `-- |-- train
        |--- test
        |--- query
        `--- info
    |-- Other_Dataset

Quick Start

Train:

Single-GPU

python tools/train_net.py --config-file ./configs/LTCC/bagtricks_R50.yml MODEL.DEVICE "cuda:0"

Multi-GPU

python tools/train_net.py --config-file ./configs/LTCC/bagtricks_R50.yml --num-gpus 4

Test:

Standard Setting: The images with the same identity and the same camera view are discarded during testing.

python tools/train_net.py --config-file ./configs/LTCC/bagtricks_R50.yml --eval-only \
MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"

Cloth-changing Setting: The images with same identity, camera view and clothes are discarded during testing.

python tools/train_net.py --config-file ./configs/LTCC/bagtricks_R50.yml --eval-only --cconly \
MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"

For more options, see python ./tools/train_net.py -h.

Baseline Results

We provide some baseline results and trained models available for download:

These models are trained for 120 epochs with batch size=64 on 2 GeForce GTX TITAN X.

Method	Backbone	Standard			Cloth-changing			download
		Rank@1	Rank@5	mAP	Rank@1	Rank@5	mAP
		Baseline	Res-50	67.55%	77.48%	32.64%	33.93%		49.49%	15.57%	-
Baseline(w/ clo)	Res-50	73.43%	81.74%	38.54%	31.89%	48.47%	15.47%	Model

License

The code is released under the Apache 2.0 license.