OSSCo

A PyTorch implementation of OSSCo based on TCSVT 2023 paper Fully Unsupervised Domain-Agnostic Image Retrieval.

Requirements

• Anaconda
• PyTorch

conda install pytorch=1.7.1 torchvision cudatoolkit=11.0 -c pytorch

• Pytorch Metric Learning

pip install pytorch-metric-learning

• THOP

pip install thop

Dataset

Cityscapes FoggyDBF and CUFSF datasets are used in this repo, you could download these datasets from official websites, or download them from MEGA. The data should be rearranged, please refer the paper to acquire the details of train/val split. The data directory structure is shown as follows:

cityscapes
   ├── train
      ├── clear (clear images)
          ├── aachen_000000_000019_leftImg8bit.png
          └── ...
          ├── bochum_000000_000313_leftImg8bit.png
          └── ...
      ├── fog (fog images)
          same structure as clear
          ...         
   ├── val
      same structure as train
  ...
cufsf
   same structure as cityscapes

Usage

python main.py or comp.py --data_name cufsf
optional arguments:
# common args
--data_root                   Datasets root path [default value is 'data']
--data_name                   Dataset name [default value is 'cityscapes'](choices='cityscapes', 'cufsf'])
--method_name                 Compared method name [default value is 'ossco'](choices=['ossco', 'simclr', 'npid', 'proxyanchor', 'softtriple', 'pretrained'])
--proj_dim                    Projected feature dim for computing loss [default value is 128]
--temperature                 Temperature used in softmax [default value is 0.1]
--batch_size                  Number of images in each mini-batch [default value is 16]
--total_iter                  Number of bp to train [default value is 10000]
--ranks                       Selected recall to val [default value is [1, 2, 4, 8]]
--save_root                   Result saved root path [default value is 'result']
# args for ossco
--style_num                   Number of used styles [default value is 8]
--gan_iter                    Number of bp to train gan model [default value is 4000]
--rounds                      Number of round to train whole model [default value is 5]

For example, to train npid on cufsf dataset, report R@1 and R@5:

python comp.py --method_name npid --data_name cufsf --batch_size 64 --ranks 1 5

to train ossco on cityscapes dataset, with 16 random selected styles:

python main.py --method_name ossco --data_name cityscapes --style_num 16

Benchmarks

The models are trained on one NVIDIA GTX TITAN (12G) GPU. Adam is used to optimize the model, lr is 1e-3 and weight decay is 1e-6. batch size is 16 for ossco, 32 for simclr, 64 for npid. lr is 2e-4 and betas is (0.5, 0.999) for GAN, other hyper-parameters are the default values.

Cityscapes

Method	Clear --> Foggy				Foggy --> Clear				Clear <--> Foggy				Download
	R@1	R@2	R@4	R@8	R@1	R@2	R@4	R@8	R@1	R@2	R@4	R@8
Pretrained	77.0	82.0	86.6	89.2	93.4	95.6	97.0	98.4	45.7	53.3	59.3	65.4	ea3u
NPID	22.8	29.4	37.2	46.4	21.6	28.4	35.6	43.6	5.9	8.3	11.2	14.1	hu2k
SimCLR	92.2	94.6	96.6	97.8	89.6	93.0	95.4	98.2	80.1	85.4	88.8	92.3	4jvm
SoftTriple	99.6	99.8	100	100	99.8	99.8	99.8	100	98.4	99.7	99.8	99.9	6we5
ProxyAnchor	99.6	100	100	100	99.6	99.8	99.8	100	98.8	99.6	99.6	99.8	99k3
OSSCo	99.2	99.6	99.8	99.8	99.2	99.4	99.4	99.6	96.9	98.9	99.4	99.5	cb2b

CUFSF

Method	Sketch --> Image				Image --> Sketch				Sketch <--> Image				Download
	R@1	R@2	R@4	R@8	R@1	R@2	R@4	R@8	R@1	R@2	R@4	R@8
Pretrained	9.0	13.1	18.1	25.6	16.6	24.1	30.7	38.2	0.3	0.3	1.3	3.0	imi4
NPID	37.2	48.7	63.8	73.4	40.7	52.8	67.8	73.9	27.1	34.4	46.0	60.1	xvci
SimCLR	24.1	39.2	56.3	72.4	32.7	45.2	56.3	68.8	15.1	21.9	33.7	49.0	xtux
SoftTriple	86.4	92.5	95.5	99.0	89.4	93.5	97.5	99.5	79.6	85.9	92.7	96.2	5qb9
ProxyAnchor	95.5	98.0	100	100	95.5	98.5	100	100	91.7	95.7	98.2	99.7	inai
OSSCo	82.4	93.5	97.5	99.5	88.4	98.0	99.5	99.5	55.0	70.4	87.2	94.5	q6ji

T-SNE (CUFSF)

Citing OSSCo

If you find OSSCo helpful, please consider citing:

@article{zheng2023fully,
  title={Fully Unsupervised Domain-Agnostic Image Retrieval},
  author={Zheng, Ziqiang and Ren, Hao and Wu, Yang and Zhang, Weichuan and Lu, Hong and Yang, Yang and Shen, Heng Tao},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2023},
  publisher={IEEE}
}