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ENet - A Neural Net Architecture for real time Semantic Segmentation

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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?

  1. 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

  1. Clone the repository and cd into it

    git clone
    cd ENet-Real-Time-Semantic-Segmentation/
  2. Use this command to train the model

    python3 --mode train -iptr path/to/train/input/set/ -lptr /path/to/label/set/
  3. Use this command to test the model

    python3 --mode test -m /path/to/the/pretrained/model.pth -i /path/to/image/to/infer.png
  4. Use

    to get more commands
    python3 --help

Some results

enet infer 1 enet infer 4 enet infer 6 enet infer 5 enet infer 2


  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.


@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" }


The code in this repository is distributed under the BSD v3 Licemse.
Feel free to fork and enjoy :)

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