OMDet-Turbo

Install

OMDet-Turbo 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; OMDet-Turbo's Swin backbone loads through transformers' TimmBackbone wrapper.

Predict

OMDet-Turbo 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("omdet-turbo")model.set_classes(["person", "dog", "skateboard"]) result = model.predict(SAMPLE_IMAGE, conf=0.3)for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

from libreyolo import LibreOpenVocab, SAMPLE_IMAGE model = LibreOpenVocab("omdet-turbo")model.set_classes(["traffic light", "bicycle"]) # OMDet-Turbo is the one family in this tier that honours iou=: its# own post-processing takes the suppression threshold as an argument,# defaulting to 0.5 when iou= is left unset.result = model.predict(SAMPLE_IMAGE, conf=0.3, iou=0.7)print(result.names, len(result))

set_classes() sets a sticky text vocabulary: call it again to replace the list outright, or skip it to keep the default COCO-80 labels, and an empty result is a valid outcome rather than an error. Unlike Grounding DINO, OMDet-Turbo decouples its class embeddings from the language task prompt, so transformers' post-processing returns labels that map straight back to the queried class list with no phrase-disambiguation step.

OMDet-Turbo has no text-token threshold: only conf filters detections, and passing text_threshold raises. It is the one family in this tier that runs its own non-maximum suppression inside post_process_grounded_object_detection, so iou is honoured here rather than warned about. 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

One checkpoint, t, the tier's only size. It mirrors omlab/omdet-turbo-swin-tiny-hf at a pinned upstream revision through transformers' OmDetTurboForObjectDetection; the mirrored weight file is byte-identical to that upstream snapshot. 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{zhao2024real,
  title={Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head},
  author={Zhao, Tiancheng and Liu, Peng and He, Xuan and Zhang, Lu and Lee, Kyusong},
  journal={arXiv preprint arXiv:2403.06892},
  year={2024}
}