Paddle

Install

# Python 3.10 to 3.12. WSL2 with Ubuntu 22.04 is the validated Windows path.pip install "libreyolo[paddle]"

python -c "from importlib.metadata import version; print(version('paddlepaddle'), version('x2paddle'), version('onnx'))"

The extra pins the exact stack the parity work measured: PaddlePaddle 2.6.2, X2Paddle 1.6.0 and ONNX 1.17 or earlier. Those pins are checked at export time, not just at install time, and a different version raises an ImportError naming the expected one. Newer Paddle releases reject parts of the static code X2Paddle 1.6.0 generates, so failing early is better than producing an artifact nobody has validated.

Export

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO9t.pt") # Writes the directory weights/LibreYOLO9t_paddlepath = model.export(format="paddle")print(path)

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

model.export(    format="paddle",    imgsz=640,        # int; this family's square canvas    batch=1,          # any other value raises ValueError    dynamic=False,    # True raises ValueError    simplify=True,    # False raises ValueError    opset=15,         # any other value raises ValueError    output_path=None, # None writes weights/<stem>_paddle)

Four arguments are fixed rather than defaulted. dynamic must be False, batch must be 1, simplify must be True for a fully static conversion graph, and opset must be 15, which is the ceiling X2Paddle 1.6.0 accepts. Passing anything else raises before tracing.

One normalization runs on the intermediate graph. ONNX defines an omitted MaxPool dilation as one, PyTorch writes the explicit all-ones attribute, and X2Paddle 1.6.0 rejects it, so the exporter removes that redundant default and leaves the specified operation unchanged.

The artifact is a directory: model.pdmodel, model.pdiparams and metadata.yaml. The Python that X2Paddle generates during conversion is not part of it.

Run the artifact

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("weights/LibreYOLO9t_paddle", device="cpu")result = model.predict(SAMPLE_IMAGE)print(result.boxes.xyxy[:3])

libreyolo predict --model weights/LibreYOLO9t_paddle \  --source https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg --device cpu --save

import numpy as npimport paddle.inference as paddle_inferimport yaml directory = "weights/LibreYOLO9t_paddle"config = paddle_infer.Config(    f"{directory}/model.pdmodel", f"{directory}/model.pdiparams")config.disable_gpu()config.disable_mkldnn()config.switch_ir_optim(False) predictor = paddle_infer.create_predictor(config)handle = predictor.get_input_handle(predictor.get_input_names()[0])handle.reshape([1, 3, 640, 640])handle.copy_from_cpu(np.zeros((1, 3, 640, 640), dtype=np.float32))predictor.run()for name in predictor.get_output_names():    print(name, predictor.get_output_handle(name).copy_to_cpu().shape) meta = yaml.safe_load(open(f"{directory}/metadata.yaml"))print(meta["model_family"], meta["task"], meta["names"]) # Preprocessing and postprocessing are yours on this path.

LibreYOLO() recognizes any directory holding both model.pdmodel and model.pdiparams, reads metadata.yaml, and returns the same Results object as the checkpoint. A device other than auto or cpu raises: this backend is CPU only.

The bare-runtime snippet mirrors what the backend configures, and the three disabled options are deliberate. The Paddle 2.6 CPU fusion pipeline can crash while optimizing the large gather and scatter graphs emitted for deformable attention, so the portable unfused static graph is the one parity was measured against. Preprocessing, decoding, NMS and coordinate rescaling become yours on that path.

Constraints

No dynamic shapes, no FP16, no INT8, no embedded NMS, no GPU runtime.

Validated combinations are YOLO9 detection, YOLO9-E2E and YOLO9-P2 detection, EC detection, pose and segmentation, RT-DETRv4, D-FINE, DEIM and DEIMv2 detection, and YOLO-NAS detection and pose. Each is covered by conversion, a CPU runtime reload, raw-output parity and matched public results.

Blocked, with the reason recorded per combination:

Anything not listed as validated or blocked is refused with the note that it has not been validated through the ONNX-to-Paddle conversion path.

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

libreyolo formats --family yolo9 --task detect