DFANet
This repo is an unofficial pytorch implementation of DFANet:Deep Feature Aggregation for Real-Time Semantic Segmentation
log
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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.
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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.
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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...
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.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
To do
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Train the backbone xceptionA on the ImageNet-1k.
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Modify the network and improve the accuracy.
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Debug and report the performance.
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Schedule the lr
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