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 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...
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
run the command 'python main.py'
curvs on CityScape set
Train the backbone xceptionA on the ImageNet-1k.
Modify the network and improve the accuracy.
Debug and report the performance.
Schedule the lr