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MMAN

This is the code for "Macro-Micro Adversarial Network for Human Parsing" in ECCV2018.

Paper link: https://arxiv.org/abs/1807.08260

By Yawei Luo, Zhedong Zheng, Liang Zheng, Tao Guan, Junqing Yu^* and Yi Yang.

^* Corresponding Author: [email protected]

The proposed framework is capable of producing competitive parsing performance compared with the state-of-the-art methods, i.e., mIoU=46.81% and 59.91% on LIP and PASCAL-Person-Part, respectively. On a relatively small dataset PPSS, our pre-trained model demonstrates impressive generalization ability.

Prerequisites

• Python 3.6
• GPU Memory >= 4G
• Pytorch 0.3.1
• Visdom

Getting started

Clone [MMAN source code] ( [email protected]:RoyalVane/MMAN.git )

Download [The LIP Dataset] Google Drive

The folder is structured as follows:

├── MMAN/
│   ├── data/                 	/* Files for data processing  		*/
│   ├── model/                 	/* Files for model    			*/
│   ├── options/          	/* Files for options    		*/
│   ├── ...			/* Other dirs & files 			*/
└── Human/
    ├── train_LIP_A/		/* Training set: RGB images		*/
    ├── train_LIP_B/		/* Training set: GT labels		*/
    ├── test_LIP_A/		/* Testing set: RGB images		*/
    └── test_LIP_B/		/* Testing set: GT labels		*/

Train

Open a visdom server

python -m visdom.server

Train a model

python train.py --dataroot ../Human --dataset LIP --name Exp_0 --output_nc 20 --gpu_ids 0 --pre_trained --loadSize 286 --fineSize 256

--dataroot The root of the training set.

--dataset The name of the training set.

--name The name of output dir.

--output_nc The number of classes. For LIP, it equals to 20.

--gpu_ids Which gpu to run.

--pre_trained Using ResNet101 model pretrained on Imagenet.

--loadSize Resize training images into 286 * 286.

--fineSize Randomly crop 256 * 256 patch from a 286 * 286 image.

Enjoy the training process in http://XXX.XXX.XXX.XXX:8097/ , where XXX is your server IP address.

Test

Use trained model to parse human images

python test.py --dataroot ../Human --dataset LIP --name Exp_0 --gpu_ids 0 --which_epoch 30 --how_many 10000 --output_nc 20 --loadSize 256

--dataroot The root of the testing set.

--dataset The name of the testing set.

--name The dir name of trained model.

--gpu_ids Which gpu to run.

--which_epoch Select the i-th model.

--how_many Total number of test images.

--output_nc The number of classes. For LIP, it equals to 20.

--loadSize Resize testing images into 256 * 256.

New! Pretrained models are available via this link:

Google Drive

Qualitative results

Trained on LIP train_set -> Tested on LIP val_set

Trained on LIP train_set -> Tested on Market1501

Citation

If you find MMAN useful in your research, please consider citing:

@inproceedings{luo2018macro,
	title={Macro-Micro Adversarial Network for Human Parsing},
	author={Luo, Yawei and 
		Zheng, Zhedong and 
		Zheng, Liang and 
		Guan, Tao and 
		Yu, Junqing and 
		Yang, Yi},
	booktitle ={ECCV},
	year={2018}
}

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