> This is the documentation for LibreYOLO 1.5.0. Current version: https://www.libreyolo.com/docs/models/ov-deim

# OV-DEIM
OV-DEIM is a DETR-style open-vocabulary object detector that matches decoder queries to text embeddings from a bundled MobileCLIP text tower. LibreYOLO ports it natively as a predict-only family in its open-vocabulary detector tier.
Tasks: Detection. Install: pip install libreyolo.
Verified against LibreYOLO v1.5.0.

## Install

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

```bash
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/`.

**Python**

```python
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)
```

**Replace the vocabulary**

```python
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](/docs/v1.5.0/predict) 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.

| File | Input (px) | Task | Weights license |
| --- | --- | --- | --- |
| `LibreOVDEIMs.pt` | 640 | Detection | cc-by-nc-4.0 |
| `LibreOVDEIMm.pt` | 640 | Detection | cc-by-nc-4.0 |
| `LibreOVDEIMl.pt` | 640 | Detection | cc-by-nc-4.0 |

## Licensing

Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice.

- Original work: OV-DEIM, Leilei Wang et al.
- Upstream license: CC BY-NC 4.0
- Upstream source: https://github.com/wleilei/OV-DEIM
- LibreYOLO code: Apache-2.0
- Weights: CC BY-NC 4.0, republished at https://huggingface.co/LibreYOLO
- Interpretation: OV-DEIM's code is Apache-2.0; LibreYOLO's port keeps that license and preserves the original RT-DETR and DEIMv2 attribution headers. The published S, M and L checkpoints carry a separate CC BY-NC 4.0 license: redistribution and format conversion are permitted with attribution, but only for non-commercial use, confirmed directly by the upstream author. Every prediction also runs a bundled MobileCLIP-B(LT) text tower, loaded unchanged from Apple's own release, to embed the vocabulary online; those weights carry the Apple Machine Learning Research Model license, which permits redistribution with the license text, an attribution notice and a record of modifications, but restricts use to research, a stricter term than CC BY-NC 4.0 that applies to every call this family makes. The `l` checkpoint's DINOv3-S backbone fine-tune is separately subject to Meta's DINOv3 License.

OV-DEIM layers three upstream licenses onto every prediction call: the
detector weights under OV-DEIM's own CC BY-NC 4.0, the online text tower
under Apple's Machine Learning Research Model license (research use only),
and, for the `l` checkpoint, a DINOv3-S backbone fine-tune under Meta's
DINOv3 License. All three license texts ship inside the LibreYOLO weight
repository.

## Citation

```bibtex
@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}, 
}
```

Copied from https://github.com/wleilei/OV-DEIM#4-citation