FoundationVision/GLEE

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1170 stars · 77 forks observed · 2026-08-28

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

GLEE is a general object foundation model for images and videos that unifies detection, segmentation, tracking, grounding, and open-world object identification in a single framework. It is a Python research codebase with pretrained checkpoints, trained on over ten million images with diverse supervision levels for strong zero-shot transfer.

Use cases

• detect and segment arbitrary objects in images with open vocabulary
• track and segment objects in videos
• segment objects referred to by a text expression in images or videos
• perform zero-shot object detection on new datasets
• interactively segment objects given visual prompts
• identify and ground objects in the open world

When to choose

• you need a single unified model for multiple object-centric vision tasks
• you want strong zero-shot generalization to unseen categories or datasets
• you need open-vocabulary detection, segmentation, or referring segmentation in one codebase
• you are doing research on object-level perception in images and videos

When to avoid

• you need a lightweight production detector with minimal compute
• you only need a simple task-specific detector like plain YOLO inference
• you cannot run GPU inference or lack PyTorch experience
• you need fine-grained classification rather than object localization and segmentation

Facets

library · maturity active

computer-vision image-processing video-processing machine-learning deep-learning computer-vision image-processing artificial-intelligence machine-learning python cross-platform object-detection object-segmentation open-vocabulary zero-shot multi-object-tracking visual-grounding foundation-model cvpr-2024 referring-expression video-segmentation video gpu

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markdown · JSON · MCP: product_card(name="FoundationVision/GLEE")