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Enet Real Time Semantic Segmentation

Enet Real Time Semantic Segmentation is a public code repository reproducing the ENet neural-network architecture for real-time semantic segmentation. It includes command-line workflows to train on image and label sets and to run inference on an image with a pretrained model, plus a Jupyter notebook linked for use with Colab.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Enet Real Time Semantic Segmentation — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you want to work directly with an ENet implementation for training or image inference, rather than use a hosted segmentation interface.

Who it is for

  • Developers exploring real-time image semantic segmentation with ENet.
  • Practitioners who have image and label datasets and want to train the repository's model.
  • Users with a pretrained model who want to run image inference.

Documented uses

  • Train a segmentation model using image inputs and corresponding labels.
  • Run semantic-segmentation inference on a single image using a pretrained model.
  • Explore the ENet implementation through its linked notebook in Colab.
Before you choose

Requirements & limits

The repository is public and its code is distributed under the BSD-3-Clause license.

  • The documented training workflow requires both an input dataset and a label dataset.
  • The inference command requires a pretrained model file and an image path.
  • The README provides a Colab-linked notebook as an alternative project entry point.
  • ENet is described as intended for real-time semantic segmentation, including mobile-device use.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run image inference with a pretrained ENet model

Use the documented test command to process one image with a pretrained model.

Before you start

  • A cloned copy of the repository
  • A pretrained model file ending in .pth
  • An image file to infer

Steps

  1. Clone the repository and change into its directory.

  2. Run the command below, replacing the paths with the model and image locations.

    Documented command
    python3 init.py --mode test -m /path/to/the/pretrained/model.pth -i /path/to/image/to/infer.png

Expected outcome

The command runs the repository's test-mode inference workflow on the specified image.

Guide limitations

  • The command does not specify output format or where results are saved.
Documentation 1 ↗
Source-based practical guide

Train ENet with image and label datasets

Start model training using paired input and label dataset paths.

Before you start

  • A cloned copy of the repository
  • A training input set and corresponding label set

Steps

  1. Clone the repository and change into its directory.

  2. Run the command below, substituting the dataset paths.

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

Expected outcome

The command starts the documented training mode with the supplied input and label paths.

Guide limitations

  • The README does not describe dataset formatting, training configuration, or completion and checkpoint behavior.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
iArunava/ENet-Real-Time-Semantic-Segmentation · Official homepage title · retained in its source language
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