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Stcn

Stcn is a Python research implementation of Space-Time Correspondence Networks for semi-supervised video object segmentation, with scripts for inference, benchmark evaluation, and training.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Stcn — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use STCN when you need a research-code workflow for evaluating, training, or applying video object segmentation with initial object masks. For an interactive GUI workflow, the README points to the MiVOS-STCN extension.

Who it is for

  • Computer-vision researchers studying video object segmentation
  • Developers who want to evaluate or adapt a Python segmentation model
  • Teams exploring propagation of supplied object masks across video frames

Documented uses

  • Evaluate pretrained STCN models on DAVIS and YouTubeVOS datasets
  • Apply segmentation to custom video when initial object masks are available
  • Train models using the documented dataset preparation workflow
Before you choose

Requirements & limits

The repository is public and specifies an MIT license.

  • Custom-video inference requires a segmentation for the first frame or when each object first appears.
  • The README lists specific versions of PyTorch, torchvision, OpenCV, and Pillow-SIMD.
  • Training requires dataset preparation; the documented BL30K download workflow can require about 1TB of free disk space.
  • The interactive GUI is associated with a separate MiVOS-STCN extension, not the STCN repository itself.

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

Evaluate a pretrained model on DAVIS 2017

Run the repository's documented DAVIS 2017 validation inference workflow.

Before you start

  • STCN repository and its listed Python package requirements
  • DAVIS data and pretrained models downloaded using the repository's scripts
  • A location for output files

Steps

  1. Prepare the DAVIS data and pretrained models using the repository instructions.

  2. Run the command below for the documented DAVIS 2017 validation default.

    Documented command
    python eval_davis.py --output [somewhere]

Expected outcome

The script evaluates the pretrained model on DAVIS 2017 validation and writes results to the specified output path.

Guide limitations

  • The documented example assumes the datasets and pretrained models have already been downloaded.
  • Changing the evaluation split is controlled by --split.
Documentation 1 ↗
Source-based practical guide

Prepare custom-video inference with existing object masks

Identify the repository's documented entry point for custom-video segmentation.

Before you start

  • Custom video data
  • A segmentation for the first frame, or a segmentation for each object when it first appears
  • The repository's listed Python requirements

Steps

  1. Provide the required initial object segmentation for the video.

  2. Use the repository's eval_generic.py entry point and consult the instructions at the top of that file.

Expected outcome

A documented route is identified for applying STCN to custom data with supplied initial masks.

Guide limitations

  • Interactive GUI use is described as part of the separate MiVOS-STCN extension.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
hkchengrex/STCN · Official homepage title · retained in its source language
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