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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.

Fuentehuggingface.co/LibreYOLO/LibreOVDEIMs/tree/6d34cfa5ab63f14f540ec1071dd8e9201240e5d8
TipoText modelVersiónmodelBcalidad

Fuente del modelo

Tipo
Text model
Tamaño del archivo
285 MB
Versión
vmodel
Actualizado
21 oct 2026
Descargas
33
Fuente
Hugging Face

Descripción de la fuente

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.

Fuentes

1 fuente
Hugging FacePrincipalVersión 6d34cfa5ab63f14f540ec1071dd8e9201240e5d8
huggingface.co/LibreYOLO/LibreOVDEIMs/tree/6d34cfa5ab63f14f540ec1071dd8e9201240e5d8

Verificado 21 oct

Artefactos del modelo

1 artefacto

model.safetensors

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

Download file

Extractos de fuentes

3 extractos
Readme5 agohuggingface.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 octhuggingface.co/LibreYOLO/LibreOVDEIMs

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

Note5 agohuggingface.co/LibreYOLO/LibreOVDEIMs

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