OWLv2

Install

OWLv2 loads through LibreYOLO's open-vocabulary detector tier, which needs the openvocab extra:

pip install "libreyolo[openvocab]"

That extra pulls in transformers and timm, the Hugging Face libraries this tier calls into.

Predict

OWLv2 is not a checkpoint LibreYOLO loads through LibreYOLO(). It loads through the sibling LibreOpenVocab factory, which downloads a Hugging Face snapshot on first use and caches it under weights/.

from libreyolo import LibreOpenVocab, SAMPLE_IMAGE model = LibreOpenVocab("owlv2-b16")model.set_classes(["person", "dog", "skateboard"]) result = model.predict(SAMPLE_IMAGE, conf=0.1)for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

from libreyolo import LibreOpenVocab, SAMPLE_IMAGE # Skipping set_classes() keeps the tier's default COCO-80 vocabulary.model = LibreOpenVocab("owlv2-l14")result = model.predict(SAMPLE_IMAGE, conf=0.1)print(result.names)

set_classes() sets a sticky text vocabulary: call it again to replace the list, or skip it to keep the default COCO-80 labels. Each label is wrapped in a fixed prompt template before it reaches the text tower, matching how transformers' Owlv2ForObjectDetection was trained.

OWLv2 has no text-token threshold: only conf filters detections, and passing text_threshold raises. iou is accepted for API compatibility but warns and does nothing, since nothing here runs non-maximum suppression. imgsz and augment=True are rejected outright: the transformers processor owns resizing, and test-time augmentation is out of scope for this tier. predict() on a single image returns one Results, not a list; pass a directory, a list of images, or stream=True for a video source to get several. There is no CLI path for this family, libreyolo predict only loads .pt checkpoints through LibreYOLO(), so LibreOpenVocab families run from Python. See prediction for source types and streaming.

Variants

Two checkpoints, b16 (base, patch size 16) and l14 (large, patch size 14). b16 is this tier's default size when none is given. Both mirror the official Google Research release through transformers' Owlv2ForObjectDetection, downloaded once into a LibreYOLO-hosted Hugging Face snapshot that preserves the upstream files. No accuracy or latency numbers are published for this family yet.

Training, dataset validation and export are all out of scope for this tier: train(), val() and export() all raise NotImplementedError unconditionally. This is a predict-only wrapper around a published checkpoint.

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{minderer2023scaling,
  title={Scaling Open-Vocabulary Object Detection},
  author={Matthias Minderer, Alexey Gritsenko, Neil Houlsby},
  journal={NeurIPS},
  year={2023},
}