OpenVINO

OpenVINO IR is Intel's runtime format, a model.xml graph beside a model.bin weight blob. LibreYOLO exports an ONNX intermediate, converts it with ov.convert_model, and writes a metadata.yaml into the same directory.

Flag: export(format="openvino")
Writes: A directory with model.xml, model.bin and metadata.yaml
Extra: pip install "libreyolo[onnx,openvino]"
Loads back: LibreYOLO("weights/LibreYOLO9t_openvino")
Shapes: Follows the ONNX intermediate: dynamic batch when dynamic=True
Precision: FP32, FP16 weight compression (half=True), INT8 via NNCF (int8=True with data=)

Install

# The IR is converted from an ONNX intermediate, so both extras are needed.pip install "libreyolo[onnx,openvino]"

INT8 additionally needs NNCF

pip install nncf

The conversion goes through an ONNX intermediate, so the onnx extra is part of the requirement rather than an optional companion. NNCF is a separate install and is only needed for int8=True.

Export

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO9t.pt") # Writes the directory weights/LibreYOLO9t_openvinopath = model.export(format="openvino")print(path)

CLI

libreyolo export --model LibreYOLO9t.pt --format openvino

Arguments

model.export(    format="openvino",    imgsz=640,    batch=1,    dynamic=False,    # True keeps a dynamic batch axis through the IR    half=False,       # True stores FP16 weights    int8=False,       # True runs NNCF post-training quantization    data=None,        # required when int8=True    output_path=None, # None writes weights/<stem>_openvino)

The artifact is a directory, not a file. weights/LibreYOLO9t_openvino holds model.xml, model.bin and metadata.yaml, and _fp16 is inserted before the suffix when half=True. Move or copy the whole directory; the three files are one artifact.

half=True sets compress_to_fp16 on save. That is weight compression in the IR, not a change to the inference precision the device chooses at run time.

INT8

INT8 with calibration data

from libreyolo import LibreYOLO LibreYOLO("LibreYOLO9t.pt").export(    format="openvino",    int8=True,    data="coco128.yaml",   # required: there is no default for this format    fraction=1.0,)

int8=True runs NNCF post-training quantization over a LibreYOLO calibration loader with the mixed preset, and data is mandatory: this format has no eight-image fallback. Missing NNCF raises an ImportError naming the install command.

Run the artifact

Through LibreYOLO

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("weights/LibreYOLO9t_openvino")result = model.predict(SAMPLE_IMAGE)print(result.boxes.xyxy[:3])

Select the device

from libreyolo import LibreYOLO # "auto" and "cpu" map to CPU, "gpu" and "cuda" map to GPU,# anything else is passed through uppercased, for example "npu" -> NPU.model = LibreYOLO("weights/LibreYOLO9t_openvino", device="gpu")

Bare OpenVINO

import numpy as npimport openvino as ovimport yaml core = ov.Core()print(core.available_devices) compiled = core.compile_model("weights/LibreYOLO9t_openvino/model.xml", "CPU")outputs = compiled(np.zeros((1, 3, 640, 640), dtype=np.float32))print([tensor.shape for tensor in outputs.values()]) # Class names, task and input size live in metadata.yaml beside the IR.meta = yaml.safe_load(open("weights/LibreYOLO9t_openvino/metadata.yaml"))print(meta["model_family"], meta["task"], meta["names"]) # Preprocessing and postprocessing are yours on this path.

LibreYOLO() recognizes any directory containing model.xml and returns the same Results object as the checkpoint, reading class names, task, input size and pose schema from metadata.yaml.

The device string is mapped rather than passed straight through. auto and cpu both compile for CPU, gpu and cuda both compile for GPU, and any other value is uppercased and handed to OpenVINO, which is how an NPU target is reached.

The third snippet is for readers with no LibreYOLO installed. Preprocessing, decoding, NMS and coordinate rescaling become yours there, and the class names only exist in metadata.yaml.

Constraints

An IR without its metadata.yaml still loads, but the backend then falls back to 80 classes and the detection task, which is wrong for anything else. Keep the directory intact.

Blocked before tracing: YOLO9 segmentation, RTMDet-Ins segmentation, SSD, Faster R-CNN and RetinaNet detection, and BiRefNet or FeyNobg matting, where OpenVINO 2026.2 cannot lower the shared matte decoder's standard ONNX DeformConv-19 operation.

Where a combination is neither validated nor blocked, the converter path is available and the project has not recorded OpenVINO runtime parity for it. Several combinations are validated with an explicit context attached, for example DeepLabV3 semantic segmentation at a fixed 520 by 520 input on OpenVINO 2026.2 with the CPU default inference precision, and L2CS gaze at a fixed 448 by 448 face crop. libreyolo formats prints that context per combination.

For the full family and task grid, see the export matrix. For one combination:

Check one family and task before exporting

libreyolo formats --family yolo9 --task detect