JosephKJ/OWOD

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1074 stars · 152 forks observed · 2026-08-28

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

OWOD is the official PyTorch implementation of ORE (Open World Object Detector), a CVPR 2021 Oral paper introducing the Open World Object Detection problem. Built on Detectron2, it detects unknown object classes without explicit supervision and incrementally learns new classes via contrastive clustering and energy-based unknown identification.

Use cases

• detect objects from classes the model was never trained on
• incrementally add new object classes to a detector without forgetting old ones
• reproduce CVPR 2021 open world object detection experiments
• research continual learning for object detection
• identify unknown instances in images as 'unknown' instead of misclassifying them
• benchmark open-set and open-world detection on COCO-style datasets

When to choose

• you need a research baseline for open-world or open-set object detection
• you want to study incremental/continual object detection with unknown identification
• you already use Detectron2 and want to extend it with energy-based unknown detection
• you need the reference implementation of the ORE paper

When to avoid

• you need a production-ready object detector with a maintained ecosystem
• you want plug-and-play detection of a fixed set of classes (use standard Detectron2 or YOLO)
• you need Windows support or easy installation without Detectron2 build steps
• you expect frequent updates or active maintenance

Facets

library · maturity maintenance

computer-vision machine-learning deep-learning computer-vision machine-learning deep-learning python object-detection open-world continual-learning incremental-learning contrastive-learning energy-based-models detectron2 research-code cvpr2021 linux gpu

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