Deformable DETR

Install

Deformable DETR needs no optional extra. Everything it imports is in the base install, using a pure-PyTorch multi-scale deformable attention core.

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.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreDeformableDETRr50.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

libreyolo predict model=LibreDeformableDETRr50.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.

Deformable DETR is inference-only in LibreYOLO. Upstream trains with Hungarian matching and a focal classification loss; that recipe is not implemented here, so train() raises NotImplementedError.

Variants

Five checkpoints cover the released configurations, all at the same input resolution. r50ss restricts attention to a single feature scale; r50ssdc5 adds a dilated C5 backbone stage on top of that. r50 is the default multi-scale configuration, sampling across four feature-map levels. r50refine adds iterative bounding box refinement across decoder layers, and r50twostage generates its initial region proposals from the encoder output instead of learned queries.

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.

from libreyolo import LibreYOLO model = LibreYOLO("LibreDeformableDETRr50.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"])

libreyolo val model=LibreDeformableDETRr50.pt data=my-dataset.yaml

Export

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.

from libreyolo import LibreYOLO model = LibreYOLO("LibreDeformableDETRr50.pt")model.export(format="onnx", imgsz=800)model.export(format="tensorrt", imgsz=800, half=True)

libreyolo export model=LibreDeformableDETRr50.pt format=onnx imgsz=800libreyolo export model=LibreDeformableDETRr50.pt format=tensorrt imgsz=800 half=True

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("LibreDeformableDETRr50.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{zhu2020deformable,
  title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
  author={Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
  journal={arXiv preprint arXiv:2010.04159},
  year={2020}
}