LW-DETR

Install

LW-DETR needs no optional extra. Everything it imports is in the base install.

pip install libreyolo

Predict

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

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

libreyolo predict model=LibreLWDETRt.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.

LW-DETR is inference-only in LibreYOLO. Upstream trains with Group-DETR one-to-many supervision across multiple query groups and an IoU-aware classification loss; that recipe is not wired here, so train() raises NotImplementedError.

Variants

Five sizes, all sharing the plain-ViT encoder, multi-scale projector and deformable DETR decoder, and all running at the same input resolution. The two smallest share an encoder width and split by block depth; the next two share a wider encoder and split by how many projector levels feed the decoder; the largest steps up to the widest encoder.

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("LibreLWDETRt.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=LibreLWDETRt.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("LibreLWDETRt.pt")model.export(format="onnx", imgsz=640)model.export(format="tensorrt", imgsz=640, half=True)

libreyolo export model=LibreLWDETRt.pt format=onnx imgsz=640libreyolo export model=LibreLWDETRt.pt format=tensorrt imgsz=640 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("LibreLWDETRt.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{chen2024lw,
    title={LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection},
    author={Chen, Qiang and Su, Xiangbo and Zhang, Xinyu and Wang, Jian and Chen, Jiahui and Shen, Yunpeng and Han, Chuchu and Chen, Ziliang and Xu, Weixiang and Li, Fanrong and others},
    journal={arXiv preprint arXiv:2406.03459},
    year={2024}
}