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Xmem

XMem is an open-source Python research implementation for long-term video object segmentation. Its Atkinson–Shiffrin-inspired memory model combines sensory, working and long-term memory to carry object information between frames. The repository provides a GUI and separate training and inference documentation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Xmem — 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 XMem when a video segmentation workflow needs object information carried across frames, especially in long clips or through occlusion, and you can work with a Python research implementation.

Who it is for

  • Computer-vision researchers studying video object segmentation and temporal consistency.
  • Developers building workflows that need object masks to persist across long videos.

Documented uses

  • Maintain an object's segmentation mask as it moves through successive video frames.
  • Study or evaluate memory-based segmentation for long clips and occlusions.
  • Use an interactive GUI for video object segmentation.
Before you choose

Requirements & limits

The repository is public and lists an MIT license.

  • The implementation is Python-based and uses PyTorch.
  • Training and inference prerequisites include required Python packages and datasets.
  • The README says videos exceeding 10,000 frames can be handled and GPU memory use is limited, while speed is hardware-dependent.
  • The repository includes a GUI and links to documented failure cases.

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

Prepare XMem for training or inference

Set up the documented prerequisites before choosing the training or inference workflow.

Before you start

  • Python packages required by the project
  • Datasets required by the project

Steps

  1. Follow the repository's GETTING_STARTED.md instructions to install the required Python packages and datasets.

  2. For training, continue with TRAINING.md; for inference, continue with INFERENCE.md.

Expected outcome

The documented prerequisites are prepared and the relevant training or inference instructions are identified.

Guide limitations

  • This guide covers prerequisite setup and navigation to the next instructions, not execution of training or inference.
  • The README does not specify hardware-independent performance.
Documentation 1 ↗
Evidence and transparency

Sources & review

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