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Segface

Segface is an open-source Python computer-vision model and research implementation that segments face images into semantic regions, with an architectural focus on improving results for less-common facial classes such as earrings and necklaces.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Segface — 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 Segface when you need a research implementation for facial-region segmentation and can work with its local Python setup, datasets and model weights.

Who it is for

  • Computer-vision researchers studying face parsing and long-tail segmentation classes.
  • Developers exploring facial-region masks for portrait editing or other image-processing workflows.
  • Practitioners comparing model variants on CelebAMask-HQ, LaPa or Helen.

Documented uses

  • Generate segmentation maps for regions in face images.
  • Explore model behavior on less-frequent classes, including earrings and necklaces.
  • Train or evaluate face-segmentation variants using the repository's supported datasets and scripts.
  • Compare documented backbone choices, including Swin, ConvNeXt, MobileNet and EfficientNet.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. Its documented workflow uses a local Conda environment; inference examples use CUDA.

  • The model targets the imbalance between frequent face regions and less-common classes through learnable class-specific transformer tokens.
  • The repository documents pretrained weights and scripts for both training and inference.
  • Dataset files must be downloaded separately and placed in the structure expected by the implementation.
  • The README reports different mean F1 results for the named datasets and backbones; these are project-reported figures.
  • The README describes a MobileNet-based option for fast inference and reports its CelebAMask-HQ result.

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

Set up the Segface training environment

Prepare the repository environment and configure the paths it uses for code, data and logs.

Before you start

  • The SegFace repository
  • Conda
  • Locations for the code, dataset and logs

Steps

  1. Create the Conda environment using the repository's environment.yml file.

  2. Activate the environment named segface.

  3. Create a .env file in the repository's main directory.

  4. Set ROOT_PATH, DATA_PATH and LOG_PATH to the relevant code, dataset and log locations.

Expected outcome

The environment is created and the repository's three path variables are configured for subsequent work.

Guide limitations

  • This setup recipe does not cover downloading or arranging datasets, training, or inference.
  • The README's example paths are specific to its authors' directory layout and should not be treated as universal paths.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
Kartik-3004/SegFace · Official homepage title · retained in its source language
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