# MMAN

**Repository Path**: shaodeyi1/MMAN

## Basic Information

- **Project Name**: MMAN
- **Description**: 在人体语义分割中，像素级别的分类损失在其低级局部不一致性和高级语义不一致性方面存在缺陷。对抗性网络的引入使用单个鉴别器来解决这两个问题。
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-08-21
- **Last Updated**: 2022-08-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


## 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<sup>*</sup> and Yi Yang. 
###### <sup>*</sup> Corresponding Author: <yjqing@hust.edu.cn>

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

Download [The LIP Dataset]( https://drive.google.com/open?id=1SlvucF37ApWCQjmdCYQ8i9yoUHNvFMiC )

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
```bash
python -m visdom.server
```

### Train a model
```bash
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
```bash
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](https://drive.google.com/open?id=1pLFXIf8o3Jpq-w4_D4l8yWE9Q-1-TVLh)

## Qualitative results
Trained on ``LIP train_set`` -> Tested on ``LIP val_set``
![](https://github.com/RoyalVane/MMAN/blob/master/jpg/LIP.JPG)

Trained on ``LIP train_set`` -> Tested on ``Market1501``
![](https://github.com/RoyalVane/MMAN/blob/master/jpg/Market1501.JPG)

## 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}
}

```

## Related Repos
1. [Pedestrian Alignment Network](https://github.com/layumi/Pedestrian_Alignment)
2. [pix2pix](https://github.com/phillipi/pix2pix)
3. [Market-1501](http://www.liangzheng.org/Project/project_reid.html)