/Single-Human-Parsing-LIP

A baseline model ( pytorch implementation, PSPNet ) for single-person human parsing task, training and evaluating on Look Into Person (LIP) dataset.

Primary LanguagePythonMIT LicenseMIT

Single-Human-Parsing-LIP

A baseline model ( PSPNet ) for single-person human parsing task, training and testing on Look into Person dataset.

Model

We built model with PyTorch 0.4.1 and the implementation of PSPNet was based on Here.

Trained model weights can be downloaded from Google Drive or Baidu Drive (提取码：43cu).

Dataset

To use our code, firstly you should download LIP dataset from Here.

Then, reorganize the dataset folder as below:

myLIP
│ 
└───train
│   │   id.txt
│   │
│   └───image
│   │   │   77_471474.jpg
│   │   │   113_1207747.jpg
│   │   │   ...
│   │
│   └───gt
│   │   │   77_471474.png
│   │   │   113_1207747.png
│   │   │   ...
│
└───val
│   │   id.txt
│   │
│   └───image
│   │   │   100034_483681.jpg
│   │   │   10005_205677.jpg
│   │   │   ...
│   │
│   └───gt
│   │   │   100034_483681.png
│   │   │   10005_205677.png
│   │   │   ...
│
└───test
│   │   id.txt
│   │
│   └───image
│   │   │   100012_501646.jpg
│   │   │   ...

Usage

python3  train.py  --data-path ~/myLIP

python3  eval.py  --data-path ~/myLIP [--visualize]

Result

model overall acc. mean acc. mean IoU
resnet50 0.792 0.552 0.463
densenet121 0.826 0.606 0.519
squeezenet 0.786 0.543 0.450

demo