ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation ITS 2017
ERFNet proposes a simple encoder-decoder architecture for real-time semantic segmentation. The core design lies in a Non-bottleneck-1D design, which starts with the Non-bottleneck (basic) block for ResNet-34/18, then replace each 3x3 conv by one 1x3 and one 3x1 convs. Although not much celebrated in the semantic segmentation task, it is widely recognized as a strong backbone for lane detection, my bet is the 1D convolution's alignment with thin line objects. However, there is a somewhat ironic problem with ERFNet: intuitively, 1x3 + 3x1 design seems only 6/9 the compute of a 3x3 conv, but with winograd, a 3x3 conv's compute can be reduced to 4/9. So the benefit of this design is only a good-looking FLOPs count (do correct me if I'm wrong). By going deeper, ERFNet shows a higher latency disproportional to its FLOPs count. Despite that, the simple encoder-decoder segmentation network provides good performance. And who knows, maybe even 1x3 convolution is getting optimized somewhere.
Training time estimated with single 2080 Ti.
ImageNet pre-training, 3-times average/best.
| backbone | resolution | training time | precision | mIoU (avg) | mIoU | |
|---|---|---|---|---|---|---|
| ResNet-101 | 512 x 1024 | 5h | mix | 71.99 | 72.47 | model | shell |
FPS is best trial-avg among 3 trials on a 2080 Ti.
| backbone | resolution | FPS | FLOPS(G) | Params(M) |
|---|---|---|---|---|
| ResNet-101 | 256 x 512 | 91.20 | 15.03 | 2.07 |
| ResNet-101 | 512 x 1024 | 85.51 | 60.11 | 2.07 |
| ResNet-101 | 1024 x 2048 | 21.53 | 240.44 | 2.07 |
@article{romera2017erfnet,
title={Erfnet: Efficient residual factorized convnet for real-time semantic segmentation},
author={Romera, Eduardo and Alvarez, Jos{\'e} M and Bergasa, Luis M and Arroyo, Roberto},
journal={IEEE Transactions on Intelligent Transportation Systems},
volume={19},
number={1},
pages={263--272},
year={2017}
}