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reimpliment of DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
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model log 2019.5.23 May 23, 2019
results log 2019.5.23 May 23, 2019
utils . Apr 10, 2019 log 2019.5.23 May 23, 2019 log 2019.5.23 May 23, 2019 . Apr 10, 2019 . Apr 10, 2019 . Apr 10, 2019 add curves of loss and iou Apr 20, 2019 log 2019.5.23 May 23, 2019 update the Apr 14, 2019 adjust lr poly Apr 28, 2019


This repo is an unofficial pytorch implementation of DFANet:Deep Feature Aggregation for Real-Time Semantic Segmentation


  • 2019.4.16 After 483 epoches it rases RuntimeError: value cannot be converted to type float without overflow: (9.85073e-06,-3.2007e-06).According to the direction of the stackoverflow the error can be fixed by modifying "self.scheduler.step()" to "self.scheduler.step(loss.cpu().data.numpy())" in

  • 2019.4.24 An function has been writed to load the pretrained model which was trained on imagenet-1k.The project of training the backbone can be Downloaded from here - Limited to my computing resources(only have one RTX2080),I trained the backbone on ILSVRC2012 with only 22 epochs.But it have a great impact on the results.

  • 2019.5.23 It's hard to improve the performance of the model.May be the model's details are different from the original paper's or the hyperparameters ....or the training strategy...or something else...


  • pytorch==1.0.0
  • python==3.6
  • numpy
  • torchvision
  • matplotlib
  • opencv-python
  • tensorflow
  • tensorboardX

Dataset and pretrained model

Download CityScape dataset and unzip the dataset into data folder.Then run the command 'python utils/' to create labels.

Train the network without pretrained model.

Modify your configuration in

run the command  'python'

curvs on CityScape set

To do

  • Train the backbone xceptionA on the ImageNet-1k.

  • Modify the network and improve the accuracy.

  • Debug and report the performance.

  • Schedule the lr

  • ...


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