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Pytorch models and toolbox for Semantic Segmentation.

Requirements

  • PyTorch >= 1.2.0
  • easydict
  • cv2
  • skimage
  • scipy
  • tensorboardX
  • ninja
  • coco python api (if use COCO dataset)

Features

Dataset

We build many dataloaders for the popular segmentation datasets, including:

  • Cityscapes
  • ADE20K
  • Pascal Context
  • COCO

Backbone

We build 5 wellknown backbone networks, i.e., DenseNet, MobileNet, ResNet, VGG, and Xception to extract feature maps.

Module

We implement many famous and useful modules for boosting semantic segmentation performance:

  • Syncronized Batch Normalization
  • Atrous Spatial Pyramid Pooling (ASPP in Deeplab)
  • Pyramid Pooling Modules (PPM in PSPNet)
  • Gaussian Dynamic Convolution

Model

We also develop the training and testing process for some published segmentation models:

  • Deeplab v3+
  • PSPNet

Optimization

There are some training tricks in this repo to boost the performance:

  • OHEM Loss
  • Poly learning rate schedules

Visualization

In order to visualize the training and testing process. We develop a Logger module. It can log training loss and can use the Tensorboard to visualize the scalar curve and the image.

Performance

We evaluate the performance (mIoU) of the models in this repo on Cityscapes validation set.

Model Papers Ours
PSPNet 77.48 77.29
DeeplabV3+ - 79.16

TODO List

  • Datasets

    • Pascal VOC 2012

  • Modules

    • Object Context Module

  • Visualization and Other Utils

    • PR-Curve
    • Segmentation Result Colorize
      • ADE20K
      • Pascal Context
      • COCO