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reimpliment of DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

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DFANet

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

log

  • 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 train.py.

  • 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 -https://github.com/huaifeng1993/ILSVRC2012. 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...

  • 2020.3.7 rewrited some code of this rep which make the code more modular(model/dfanet.py).

Installation

  • 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/preprocess_data.py' to create labels.

Train the network without pretrained model.

Modify your configuration in main.py.

run the command  'python main.py'

curvs on CityScape set

#

inference speed

platform input size batch size inference time /ms
rk3399 320*200 1 960
rk3399 200*100 1 589
rk3399 80*80 1 259
rk3399 72*72 1 161
2080 1024*1024 4 40
2080 1024*1024 1 16
2080 2048*1024 1 17
2080 2048*1024 2 39
2080 512*512 1 39
2080 512*512 16 44

Some experimental results was provided by @ShaoqingGong

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

  • ...

Thanks

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reimpliment of DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

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