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DFANet

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

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

Modify your configuration in main.py.

run the command  'python main.py'

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