Practical overview
When it fits
Use it when you want a modifiable PyTorch reference codebase for semantic-segmentation experiments, especially when working with Cityscapes or CamVid or comparing several included architectures.
Who it is for
- Computer-vision researchers comparing semantic-segmentation architectures.
- Python developers prototyping or modifying lightweight segmentation models.
- Practitioners working with the documented Cityscapes or CamVid dataset workflows.
Documented uses
- Review and modify reference implementations of multiple segmentation architectures.
- Train and test models using the documented Cityscapes and CamVid workflows.
- Run the documented Cityscapes prediction workflow.
- Explore listed losses, optimizers, activations and learning-rate schedulers in segmentation experiments.
Requirements & limits
The public GitHub repository is listed under the MIT License.
- The implementation requires Python 3, PyTorch 1.1.0 or later and Torchvision 0.3.0 or later.
- Cityscapes preparation requires the dataset, Cityscapes scripts, training-ID annotation creation and the expected directory layout.
- The README documents CamVid for training and testing, while its prediction instructions specify Cityscapes.
- The repository exposes command-line entry points, but users need to inspect each command’s help and project instructions for model-specific options.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare a Cityscapes dataset for the repository
Set up the documented Cityscapes annotations and folder name needed before using the project’s Cityscapes workflows.
Before you start
- Python 3
- Cityscapes dataset
- Cityscapes scripts
- PyTorch 1.1.0 or later and Torchvision 0.3.0 or later
Steps
Download Cityscapes and its scripts.
Run createTrainIdLabelImgs.py to create annotations based on the training labels.
Name the dataset folder cityscapes and arrange its image and annotation folders to match the repository’s stated structure.
Expected outcome
A Cityscapes directory prepared for the repository’s documented training, testing, prediction and evaluation workflows.
Guide limitations
- The repository documentation does not provide dataset credentials or reproduce the full preparation procedure here.
- This guide covers preparation only, not a completed training or prediction run.
Find the repository’s training, test and prediction entry points
Locate the documented command-line entry points for supported Cityscapes and CamVid workflows.
Before you start
- A local checkout of the repository
- Python 3
- PyTorch 1.1.0 or later and Torchvision 0.3.0 or later
- Prepared Cityscapes or CamVid data for the relevant workflow
Steps
Clone the repository and change into its directory.
Use python train.py --help to inspect the training command options.
Use python test.py --help to inspect the testing command options.
For Cityscapes prediction, use python predict.py --help to inspect prediction options.
Expected outcome
The repository’s documented command entry points and their available options are presented for inspection.
Guide limitations
- Prediction is documented for Cityscapes; training and testing are documented for Cityscapes and CamVid.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- xiaoyufenfei/Efficient-Segmentation-Networks · Official homepage title · retained in its source language