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LibreOVDEIMs

OV-DEIM-S real-time open-vocabulary detector (11M params, ViT-tiny (DEIMv2 distilled) backbone) repackaged for LibreYOLO's LibreOpenVocab tier, bundled with the MobileCLIP-B(LT) text tower for online prompt encoding.

Fontehuggingface.co/LibreYOLO/LibreOVDEIMs/tree/6d34cfa5ab63f14f540ec1071dd8e9201240e5d8
TipoText modelVersãomodelBnota

Fonte do modelo

Tipo
Text model
Tamanho do arquivo
285 MB
Versão
vmodel
Atualizado
21 de out. de 2026
Downloads
33
Fonte
Hugging Face

Descrição da fonte

LibreOVDEIMs

OV-DEIM-S real-time open-vocabulary detector (11M params, ViT-tiny (DEIMv2 distilled) 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_s.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 161 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-s")
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.

Fontes

1 fonte
Hugging FacePrimárioVersão 6d34cfa5ab63f14f540ec1071dd8e9201240e5d8
huggingface.co/LibreYOLO/LibreOVDEIMs/tree/6d34cfa5ab63f14f540ec1071dd8e9201240e5d8

Verificado 21 de out.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 285 MB · SHA-256 5948e74c37db…b0af · Hugging Face

Download file

Trechos de fonte

3 trechos
Readme5 de ago.huggingface.co/LibreYOLO/LibreOVDEIMs

--- license: cc-by-nc-4.0 library_name: libreyolo tags: - object-detection - zero-shot-object-detection - open-vocabulary-detection - libreyolo --- # LibreOVDEIMs OV-DEIM-S real-time open-vocabulary detector (11M params, ViT-tiny (DEIMv2 distilled) 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](https://github.com/wleilei/OV-DEIM) at commit `dfbf394672407b7f837ec08e7d68e8127548b254` (checkpoint `ovdeim_s.pth`, trained on Objects365v1 + GoldG). Text encoder taken from [apple/MobileCLIP-B-LT-OpenCLIP](https://huggingface.co/apple/MobileCLIP-B-LT-OpenCLIP). Paper: OV-DEIM: Real-time DETR-Style Open-Vocabulary Object Detection with GridSynthetic Augmentation (arXiv 2603.07022). Upstream reports 161 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](https://github.com/LibreYOLO/libreyolo). LibreYOLO's port reproduces the upstream paper's zero-shot COCO val2017 result through this artifact (45.9 AP for the L size). ## Usage ```python from libreyolo import LibreOpenVocab model = LibreOpenVocab("ov-deim-s") 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](./LICENSE) (non-commercial, attribution; redistribution and format conversion permitted by upstream's MODEL_LICENSE). - OV-DEIM code lineage: [Apache-2.0](./LICENSE_CODE). - MobileCLIP-B(LT) text tower: [Apple Machine Learning Research Model license](./LICENSE_MOBILECLIP_WEIGHTS), research use only. - DINOv3-derived backbone: [DINOv3 License](./LICENSE_DINOV3). See [`NOTICE`](./NOTICE) for attribution details.

Note21 de out.huggingface.co/LibreYOLO/LibreOVDEIMs

Source context: 33 downloads · 0 likes · Pipeline object-detection · Library libreyolo · Repo LibreYOLO/LibreOVDEIMs

Note5 de ago.huggingface.co/LibreYOLO/LibreOVDEIMs

Source context: 28 downloads · 0 likes · Pipeline object-detection · Library libreyolo · Repo LibreYOLO/LibreOVDEIMs