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.
-
[x] Schedule the lr
-
[ ] ...