guidograzioli/enet-real-time-semantic-segmentation

ENet - A Neural Net Architecture for real time Semantic Segmentation

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?

0. 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

---

0. 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/

1. Use this command to train the model

python3 init.py --mode train -iptr path/to/train/input/set/ -lptr /path/to/label/set/

2. 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

3. Use --help to get more commands

python3 init.py --help

Some results

Pretrained models

We plan to open source more pretrained models which are better.
For now, we have only open sourced one pretrained model, which is trained on CamVid dataset.
Find it here: Pretrained ENet on CamVid

References

1. 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 :)