LibreOVDEIMl
OV-DEIM-L real-time open-vocabulary detector (36M params, DINOv3-S/16 (Meta, fine-tuned)
backbone) repackaged for LibreYOLO's LibreOpenVocab tier, bundled with the
MobileCLIP-B(LT) text tower for online prompt encoding.
Source
Detector converted from wleilei/OV-DEIM
at commit dfbf394672407b7f837ec08e7d68e8127548b254 (checkpoint
ovdeim_l.pth, trained on Objects365v1 + GoldG). Text encoder taken from
apple/MobileCLIP-B-LT-OpenCLIP.
Paper: OV-DEIM: Real-time DETR-Style Open-Vocabulary Object Detection with GridSynthetic Augmentation (arXiv 2603.07022). Upstream reports 91 FPS on a T4 with TensorRT at 640 input.
Modifications
State-dict key remapping and merge only (module. prefix stripped, the
training-only denoising embedding dropped, the MobileCLIP text tower stored
under text_encoder.*). Learned parameters are unchanged. See
weights/convert_ovdeim_weights.py in the
LibreYOLO source repository.
LibreYOLO's port reproduces the upstream paper's zero-shot COCO val2017 result through this artifact (45.9 AP for the L size).
Usage
from libreyolo import LibreOpenVocab
model = LibreOpenVocab("ov-deim-l")
model.set_classes(["person", "a red backpack", "forklift"])
results = model.predict("image.jpg")
License
Layered, most restrictive governs the combined artifact:
- Detector weights: CC BY-NC 4.0 (non-commercial, attribution; redistribution and format conversion permitted by upstream's MODEL_LICENSE).
- OV-DEIM code lineage: Apache-2.0.
- MobileCLIP-B(LT) text tower: Apple Machine Learning Research Model license, research use only.
- DINOv3-derived backbone: DINOv3 License.
See NOTICE for attribution details.