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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.

    Image  Image

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

  • PSPNet
  • DeepLabV3
  • CCNet
  • DANet
  • OCNet
  • CE2P
  • HRNet
  • BraidNet

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}
}