Practical overview
When it fits
Use it when you want to follow the repository’s documented training paths for CamVid or Cityscapes and can work with its specified PyTorch 0.2.0 environment.
Who it is for
- Computer-vision learners studying fully convolutional networks.
- Practitioners building semantic-segmentation prototypes with CamVid or Cityscapes.
Documented uses
- Prepare CamVid or Cityscapes data using the repository’s utility scripts.
- Start semantic-segmentation model training for either dataset.
Requirements & limits
The implementation is available as a public GitHub repository and identifies itself as open source.
- The documented dependency is PyTorch 0.2.0.
- CamVid is the default dataset; Cityscapes has a separate preparation script.
- The README reports dataset-specific pixel accuracy, but does not establish performance on other data or environments.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare and train with CamVid
Follow the repository’s documented CamVid training workflow.
Before you start
- Python 3 and packages listed in requirements.txt
- PyTorch 0.2.0
- CamVid dataset
Steps
Install the listed packages with pip3 install -r requirements.txt.
Download CamVid and place its data in a directory named CamVid.
Run python3 python/CamVid_utils.py.
Run python3 python/train.py CamVid.
Expected outcome
The README provides commands to prepare CamVid data and start model training.
Guide limitations
- The instructions specify PyTorch 0.2.0.
- The README’s reported CamVid accuracy is not a guarantee for other environments or data.
Prepare and train with Cityscapes
Follow the repository’s documented Cityscapes training workflow.
Before you start
- Python 3 and packages listed in requirements.txt
- PyTorch 0.2.0
- Cityscapes dataset
Steps
Install the listed packages with pip3 install -r requirements.txt.
Download Cityscapes and place its data in a directory named CityScapes.
Run python3 python/CityScapes_utils.py.
Run python3 python/train.py CityScapes.
Expected outcome
The README provides commands to prepare Cityscapes data and start model training.
Guide limitations
- The instructions specify PyTorch 0.2.0.
- The README’s reported Cityscapes accuracy is not a guarantee for other environments or data.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 18 October 2026
- Original website title
- pochih/FCN-pytorch · Official homepage title · retained in its source language