DeepLab_V3_plus : a model about semantic segmentation

This is a simple pytorch re-implementation of Google Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Introduction:

This work still need to be updated. The features are summarized blow:

• Use ResNet101 as base Network. Xception will be updated soon.
• Use only VOC2012 for base dataset. Other dataset will be updated soon.

We have finished:

• Version avaliable for VOC2012.

• You can check your dataloader error in 'path/to/workspace/check/check_dataloader/img'. You will see three part:

  • 1.original image which we load directly from image path.
  • 2.restore image from torch-tensor(transformed from numpy) back to numpy ndarray.
  • 3.mask loaded by dataloader. Following images show original image, augumentation image and mask target from left to right
  
  
  

• Network architecture.

  

• Evaluation mIOU on PASCAL VOC2012 valset every epoches.

• Pretrained model on PASCAL VOC2012 BaiduYun Link, which is trained on 2 Tesla P100 for 100 epoches with config shown in code.

Usage:

• Download dataset and unzip

ln -s VOCdevkit path/to/deeplab_v3_plus/dataset

• Pretrained model is avaliable BaiduYun Link
• Finally, run the model.
• Check you GPU resources and modify your run.sh.

sh run.sh

Future:

• Xception as Network Baseline.
• Pretrained model on COCO, JFT.
• Depthwise separable convolution.
• Support dataset for Cityscapes.
• Visualization for test result and gt.