OV-DEIM

Install

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

pip install "libreyolo[openvocab]"

Unlike the rest of this tier, OV-DEIM is a native LibreYOLO port rather than a transformers wrapper, no transformers model class exists for it, but the same extra covers the huggingface_hub, safetensors, regex and ftfy packages it needs at predict time.

Predict

OV-DEIM 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("ov-deim-s")model.set_classes(["person", "dog", "skateboard"]) result = model.predict(SAMPLE_IMAGE, conf=0.25)for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

from libreyolo import LibreOpenVocab, SAMPLE_IMAGE model = LibreOpenVocab("ov-deim-l")model.set_classes(["traffic light", "bicycle"])first = model.predict(SAMPLE_IMAGE, conf=0.3) # A second call to set_classes() replaces the vocabulary outright and# re-embeds it through the text tower; an empty result is a valid# outcome rather than an error.model.set_classes(["giraffe"])second = model.predict(SAMPLE_IMAGE, conf=0.5)print(second.names, len(second))

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. Each decoder query is scored by cosine similarity against text embeddings from a bundled MobileCLIP-B(LT) text tower, computed online for whatever vocabulary is set and cached until it changes, so arbitrary prompts work without any precomputed embedding file.

OV-DEIM has no text-token threshold: only conf filters detections, and passing text_threshold raises. Matching is one-to-one top-K selection, so nothing here runs non-maximum suppression, and iou is accepted for API compatibility but warns and does nothing. imgsz and augment=True are rejected outright: the model owns a fixed letterboxed input, 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.

Every call to predict() also runs the bundled MobileCLIP-B(LT) text tower to embed the current vocabulary; see Licensing for what that adds to the terms.

Variants

Three checkpoints, s, m and l. s is this tier's default size when none is given. Unlike the rest of this tier, OV-DEIM is a native port rather than a transformers wrapper: LibreYOLO vendors the detector modules under the same Apache-2.0 license as the upstream code and reuses the DINOv3 backbone adapter already built for the DEIMv2 family. The l checkpoint's backbone is a DINOv3-S fine-tune, licensed separately under Meta's DINOv3 License. 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

@misc{wang2026ovdeim,
      title={OV-DEIM: Real-time DETR-Style Open-Vocabulary Object Detection with GridSynthetic Augmentation}, 
      author={Leilei Wang and Longfei Liu and Xi Shen and Xuanlong Yu and Ying Tiffany He and Fei Richard Yu and Yingyi Chen},
      year={2026},
      eprint={2603.07022},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.07022}, 
}