hkchengrex/Tracking-Anything-with-DEVA

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1508 stars · 142 forks observed · 2026-08-28

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

DEVA is a decoupled video segmentation framework that combines task-specific image-level segmentation models with a universal bi-directional temporal propagation model to produce coherent video segmentation. It supports open-vocabulary, text-prompted tracking of arbitrary objects in videos and can integrate custom image segmentation models without finetuning.

Use cases

• segment and track objects in videos with text prompts
• open-vocabulary video segmentation without task-specific video training data
• propagate image segmentation masks across video frames
• apply Segment Anything or GroundingDINO to long videos
• video object segmentation for research benchmarks like DAVIS
• integrate a custom image segmentation model into video tracking

When to choose

• you need open-vocabulary or open-world video segmentation with text prompts
• you want to reuse an image-level segmentation model on videos without finetuning
• you need long-term coherent tracking of many objects in research settings

When to avoid

• you need a lightweight real-time production video pipeline
• you lack a GPU or cannot run large deep-learning models
• you need a turnkey end-user application rather than a research codebase

Facets

library · maturity stable

computer-vision image-processing machine-learning deep-learning computer-vision image-processing artificial-intelligence deep-learning python cross-platform video-segmentation object-tracking open-vocabulary segment-anything video-object-segmentation research iccv-2023 pytorch video linux gpu

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