PIDNet

Install

PIDNet 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. The -sem filename suffix is required for this family.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibrePIDNets-sem.pt")result = model(SAMPLE_IMAGE, save=True) mask = result.semantic_maskprint(mask.data.shape)   # (H, W) class idsprint(mask.classes)      # sorted class ids present in the image

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

Semantic segmentation returns one class id per pixel, not boxes, so result.semantic_mask carries a (H, W) array on .data and the list of class ids present in the image on .classes. conf, iou and max_det are accepted for API parity but have no effect: the model assigns a class to every pixel by argmax, with no confidence threshold or NMS step. See prediction for sources, streaming and result handling.

Variants

Three sizes, all at a fixed 1024 px input. The published checkpoints are conversions of the official PIDNet Cityscapes weights, 19 classes.

LibreYOLO does not train PIDNet: train() raises NotImplementedError for this family, which the support tier above marks as inference only.

Validate

val() returns metrics/mIoU and metrics/pixel_accuracy, measured against any dataset in the format you trained on.

from libreyolo import LibreYOLO model = LibreYOLO("LibrePIDNets-sem.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mIoU"])print(metrics["metrics/pixel_accuracy"])

libreyolo val model=LibrePIDNets-sem.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("LibrePIDNets-sem.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibrePIDNets-sem.pt format=onnxlibreyolo export model=LibrePIDNets-sem.pt format=tensorrt 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("LibrePIDNets-sem.onnx")result = model(SAMPLE_IMAGE) print(result.semantic_mask.data.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@misc{xu2022pidnet,
      title={PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller}, 
      author={Jiacong Xu and Zixiang Xiong and Shankar P. Bhattacharyya},
      year={2022},
      eprint={2206.02066},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}