hkchengrex/XMem

Health v2 · maintenance only

Adoption not part of the score

1983 stars · 212 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

XMem is a PyTorch model for semi-supervised video object segmentation that tracks objects through long videos using an Atkinson-Shiffrin-inspired multi-store feature memory. It handles videos of 10,000+ frames at roughly 20 FPS with bounded GPU memory and includes an interactive GUI for annotating frames.

Use cases

• segment objects in long videos with a mask on the first frame
• track objects through occlusions in video
• annotate video frames with an interactive segmentation GUI
• run video object segmentation on very long clips without running out of GPU memory
• insert masked layers into video footage
• benchmark video object segmentation on DAVIS and long-video datasets

When to choose

• you need semi-supervised VOS on videos longer than a minute
• GPU memory is a constraint for long-video segmentation
• you want a fast (~20 FPS) segmentation model with an interactive annotation GUI
• you need a research-grade ECCV 2022 baseline for VOS

When to avoid

• you need fully automatic, prompt-free video segmentation
• you only need image (single-frame) segmentation
• you want a production video-editing tool rather than a research model
• you lack a GPU, as inference speed depends heavily on CUDA hardware

Facets

library · maturity stable

computer-vision image-processing machine-learning deep-learning computer-vision deep-learning machine-learning python cross-platform video-object-segmentation pytorch eccv-2022 memory-model semi-supervised-segmentation gui-demo video gpu linux

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markdown · JSON · MCP: product_card(name="hkchengrex/XMem")