ENet - Real Time Semantic Segmentation
A Neural Net Architecture for real time Semantic Segmentation.
In this repository we have reproduced the ENet Paper - Which can be used on
mobile devices for real time semantic segmentattion. The link to the paper can be found here: ENet
How to use?
- This repository comes in with a handy notebook which you can use with Colab.
You can find a link to the notebook here: ENet - Real Time Semantic Segmentation
Open it in colab: Open in Colab
- Clone the repository and cd into it
git clone https://github.com/iArunava/ENet-Real-Time-Semantic-Segmentation.git
cd ENet-Real-Time-Semantic-Segmentation/
- Use this command to train the model
python3 init.py --mode train -iptr path/to/train/input/set/ -lptr /path/to/label/set/
- Use this command to test the model
python3 init.py --mode test -m /path/to/the/pretrained/model.pth -i /path/to/image/to/infer.png
- Use
--helpto get more commands
python3 init.py --help
Some results
References
- A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello. Enet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147, 2016.
Citations
@inproceedings{ BrostowSFC:ECCV08,
author = {Gabriel J. Brostow and Jamie Shotton and Julien Fauqueur and Roberto Cipolla},
title = {Segmentation and Recognition Using Structure from Motion Point Clouds},
booktitle = {ECCV (1)},
year = {2008},
pages = {44-57}
}
@article{ BrostowFC:PRL2008,
author = "Gabriel J. Brostow and Julien Fauqueur and Roberto Cipolla",
title = "Semantic Object Classes in Video: A High-Definition Ground Truth Database",
journal = "Pattern Recognition Letters",
volume = "xx",
number = "x",
pages = "xx-xx",
year = "2008"
}
License
The code in this repository is distributed under the BSD v3 Licemse.
Feel free to fork and enjoy :)