RF-DETR

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

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

from libreyolo import LibreYOLO model = LibreYOLO("LibreRFDETRs.pt") # Any source the library accepts: file, folder, URL, webcam index,# RTSP stream, or a .streams listfor result in model.predict("clip.mp4", stream=True, save=True):    print(len(result.boxes))

from libreyolo import LibreYOLO model = LibreYOLO("LibreRFDETRs.pt")model.train(data="my-dataset.yaml", epochs=50, imgsz=512, batch=8, lr0=1e-4)

libreyolo train model=LibreRFDETRs.pt data=my-dataset.yaml \  epochs=50 imgsz=512 batch=8 lr0=1e-4

from libreyolo import LibreYOLO model = LibreYOLO("LibreRFDETRs.pt")model.train(data="my-dataset.yaml", epochs=50, lora=True)

libreyolo train model=LibreRFDETRs.pt data=my-dataset.yaml \  epochs=50 device=0,1 batch=-1

from libreyolo import LibreYOLO model = LibreYOLO("LibreRFDETRs.pt") # val() returns a plain dict, not an objectmetrics = model.val(data="my-dataset.yaml", imgsz=512) print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])print(metrics["metrics/precision"], metrics["metrics/recall"])

libreyolo val model=LibreRFDETRs.pt data=my-dataset.yaml imgsz=512

# The bundled COCO yaml carries an embedded download script, so it# needs explicit permission unless the dataset is already local.libreyolo val model=LibreRFDETRn.pt data=coco.yaml imgsz=384 \  allow_download_scripts=True

from libreyolo import LibreYOLO model = LibreYOLO("LibreRFDETRs.pt")model.export(format="onnx", imgsz=512)model.export(format="tensorrt", imgsz=512, half=True) # Arguments accepted for every format:##   format    "onnx" | "torchscript" | "executorch" | "tensorrt"#             | "openvino" | "paddle" | "mnn" | "rknn" | "ncnn"#             | "tflite" | "coreml" | "coreai".#             "engine" is an alias for tensorrt, "litert" for tflite.#   imgsz     int, or (height, width). Defaults to the checkpoint's#             native resolution.#   batch     int, default 1.#   half      bool, export in FP16. Default False.#   int8      bool, export in INT8. Default False. Needs `data`.#   data      path to a dataset YAML, used to calibrate int8.#   fraction  float, share of that calibration set to use. Default 1.0.#   dynamic   bool, dynamic axes. Default True.#   simplify  bool, run ONNX graph simplification. Default True.#   opset     int, ONNX opset. Chosen per family when not given.#   device    str, device to trace on. Defaults to the model's device.#   output_path  str, defaults to a name derived from the checkpoint.#   verbose   bool, default False.#   allow_download_scripts  bool, default False. Permits embedded#             Python in a dataset YAML that has to be downloaded.## A few formats take extra arguments of their own, such as an RKNN# target platform. Those are documented on each format's page.

libreyolo export model=LibreRFDETRs.pt format=onnx imgsz=512libreyolo export model=LibreRFDETRs.pt format=tensorrt imgsz=512 half=True

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

import numpy as npimport onnxruntime as ort # Running the graph directly means doing your own preprocessing and# postprocessing. Inspect the signature before wiring anything up.session = ort.InferenceSession("LibreRFDETRs.onnx")name = session.get_inputs()[0].nameoutputs = session.run(None, {name: np.zeros((1, 3, 512, 512), dtype=np.float32)}) for meta, array in zip(session.get_outputs(), outputs):    print(meta.name, array.shape)