Awesome

parsing_platform

A pytorch codebase for human parsing and vehicle parsing.

Introduction

A pytorch codebase for human parsing and vehicle parsing. The introduction of our new MVP dataset for vehicle parsing can be found HERE.

Requirements

Linux or macOS with python ≥ 3.6
PyTorch = 0.4.1
torchvision that matches the Pytorch installation. You can install them together at pytorch.org to make sure of this.
tensorboard (needed for visualization): pip install tensorboard

Supported methods

Supported datasets

Look-Into-Person LIP
Multi-grained Vehicle Parsing MVP

Train and Test

The scripts to train and test models are in train_test. The scripts for PSPNet, DeepLabV3, and HRNet are ready for directly running. The train/val/test splitting files used in our experiments can be found here.

Model Zoo

Models trained on the MVP dataset for vehicle parsing:

Method	Dataset	Pixel Acc	Mean Acc	mIoU	download
PSPNet	MVP-Coarse	90.26%	89.08%	79.78%	model
PSPNet	MVP-Fine	86.21%	69.61%	57.47%	model
DeepLabV3	MVP-Coarse	90.55%	89.45%	80.41%	model
DeepLabV3	MVP-Fine	87.42%	73.50%	61.60%	model
HRNet	MVP-Coarse	90.40%	89.36%	80.04%	model
HRNet	MVP-Fine	86.47%	72.62%	60.21%	model

* The performance is evaluated on the test set.

** The PSPNet and HRNet models are trained with cross-entropy loss. The DeepLabV3 models are trained with cross-entropy + IoU loss.

*** We also released several pre-trained model on the LIP dataset. Please refer to models.

Citation


@inproceedings{mm/LiuZLSM19,
  author    = {Xinchen Liu and
               Meng Zhang and
               Wu Liu and
               Jingkuan Song and
               Tao Mei},
  title     = {BraidNet: Braiding Semantics and Details for Accurate Human Parsing},
  booktitle = ACM MM,
  pages     = {338--346},
  year      = {2019}
}

@inproceedings{mm/LiuLZY020,
  author    = {Xinchen Liu and
               Wu Liu and
               Jinkai Zheng and
               Chenggang Yan and
               Tao Mei},
  title     = {Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle
               Re-identification},
  booktitle = {ACM MM},
  pages     = {907--915},
  year      = {2020}
}