DINO-DETR

DINO-DETR, published by IDEA Research as DINO, combines contrastive denoising training with mixed query selection on top of Deformable DETR's sparse attention. LibreYOLO ships three sizes for detection, inference only.

Tasks: detection
Sizes: r50, r50s5, swinl at 800 px
Install: pip install libreyolo
Support tier: Inference only, since v. Predict, validate and export only. Training features do not apply.
Upstream: DINO-DETR by IDEA Research, Apache-2.0. Paper, source
Licenses: Code Apache-2.0, weights Apache-2.0. Commercial use

Install

DINO-DETR needs no optional extra. Everything it imports is in the base install, using the same pure-PyTorch multi-scale deformable attention core as LibreYOLO's Deformable DETR family.

bash

pip install libreyolo

Installing libreyolo[hub-kernels] is optional. Once the kernels package is present, LibreYOLO fetches a compiled multi-scale deformable attention kernel from the Hugging Face Hub at runtime and uses it in place of the pure-PyTorch core; LIBREYOLO_HUB_KERNELS=0 turns it back off.

Predict

Weights download from Hugging Face on first use and are cached locally.

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreDINODETRr50.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

CLI

libreyolo predict model=LibreDINODETRr50.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg save=True

The returned Results object is the one every family returns, so swapping in a different detector is a one line change. conf and max_det filter the query selection; iou is accepted for API parity but has no effect, because the decoder is a set predictor with no NMS step. See prediction for sources, streaming and result handling.

DINO-DETR is inference-only in LibreYOLO. Upstream trains with contrastive denoising and Hungarian matching; that recipe is not implemented here, so train() raises NotImplementedError.

Variants

Three checkpoints, all at the same input resolution. r50 and r50s5 share a ResNet-50 backbone and differ in how many feature-map scales feed the decoder, four against five. swinl swaps the backbone for Swin-L and also samples five scales.

Validate

val() returns a dictionary of metrics/ keys covering precision, recall, mAP 50 and mAP 50-95, measured against any dataset in the format you trained on.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreDINODETRr50.pt") # val() returns a plain dict, not an objectmetrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])print(metrics["metrics/precision"], metrics["metrics/recall"])

CLI

libreyolo val model=LibreDINODETRr50.pt data=my-dataset.yaml

Export

Task	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
Detection	Detection to ONNX: supported.	Detection to TorchScript: supported.	Detection to ExecuTorch: supported.	Detection to TensorRT: supported.	Detection to OpenVINO: supported.	Detection to Paddle: not supported	Detection to MNN: not supported	Detection to RKNN: not supported	Detection to ncnn: not supported	Detection to TFLite: not supported	Detection to CoreML: not supported	Detection to Core AI: not supported

An exported artifact loads back through LibreYOLO() on its file suffix, so a .onnx or .engine file behaves like a checkpoint and returns the same Results. Export lists the arguments every format accepts.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreDINODETRr50.pt")model.export(format="onnx", imgsz=800)model.export(format="tensorrt", imgsz=800, half=True)

CLI

libreyolo export model=LibreDINODETRr50.pt format=onnx imgsz=800libreyolo export model=LibreDINODETRr50.pt format=tensorrt imgsz=800 half=True

Use the exported file

from libreyolo import LibreYOLO, SAMPLE_IMAGE # The factory routes on the file suffix, so an exported artifact loads# like any checkpoint and returns the same Results object.model = LibreYOLO("LibreDINODETRr50.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
Detection
LibreDINODETRr50.pt	800	apache-2.0
LibreDINODETRr50s5.pt	800	apache-2.0
LibreDINODETRswinl.pt	800	apache-2.0

Every file above exists in the LibreYOLO org today and downloads on first use.

Licensing

Check the license on the Hugging Face repository of the specific weights you download. Every checkpoint in the LibreYOLO org carries one, and they are not always the same across a family. That repository is the authoritative source; the summary below describes what applied when this page was last verified.

This is a description of the licenses involved, not legal advice. If the answer matters commercially, read the licenses yourself and take your own counsel.

Original work: DINO-DETR, IDEA Research
Upstream license: Apache-2.0
Upstream source: github.com/IDEA-Research/DINO
LibreYOLO code: MIT
Weights: Apache-2.0, republished at huggingface.co/LibreYOLO
Interpretation: Apache-2.0 is a permissive license, so these weights can be used in commercial and closed-source products. It asks you to keep its license text and attribution notices with any copy of the weights you redistribute, and it grants a patent license. It places no obligation on your own application code, and weights you train yourself on your own data are yours. The three checkpoints come from the authors' Google Drive release rather than a Hugging Face model card, and the upstream repository does not attach a license to the checkpoint files individually, so the redistribution basis is the repository-level Apache-2.0 declaration rather than a checkpoint-specific grant.

The three official checkpoints come from the authors' Google Drive release folder, not a Hugging Face model card. The upstream repository declares Apache-2.0 at the repository level but does not attach a license file or license metadata to the checkpoints themselves, so the redistribution basis is that repository-level declaration rather than a checkpoint-specific grant. Every LibreYOLO mirror ships the verbatim upstream Apache-2.0 license text alongside a notice explaining this.

Citation

@misc{zhang2022dino,
      title={DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection}, 
      author={Hao Zhang and Feng Li and Shilong Liu and Lei Zhang and Hang Su and Jun Zhu and Lionel M. Ni and Heung-Yeung Shum},
      year={2022},
      eprint={2203.03605},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{li2022dn,
      title={Dn-detr: Accelerate detr training by introducing query denoising},
      author={Li, Feng and Zhang, Hao and Liu, Shilong and Guo, Jian and Ni, Lionel M and Zhang, Lei},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      pages={13619--13627},
      year={2022}
}

@inproceedings{
      liu2022dabdetr,
      title={{DAB}-{DETR}: Dynamic Anchor Boxes are Better Queries for {DETR}},
      author={Shilong Liu and Feng Li and Hao Zhang and Xiao Yang and Xianbiao Qi and Hang Su and Jun Zhu and Lei Zhang},
      booktitle={International Conference on Learning Representations},
      year={2022},
      url={https://openreview.net/forum?id=oMI9PjOb9Jl}
}

Copied from the authors' citation block at github.com/IDEA-Research/DINO#bibtex.