PicoDet

Install

PicoDet needs no extra beyond the base package.

pip install libreyolo

Predict

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

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

libreyolo predict model=LibrePICODETs.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 sets the confidence threshold and iou the NMS threshold. See prediction for sources, streaming and result handling.

Variants

Three sizes, each at its own fixed input resolution: s the smallest and l the largest. Resolution grows with the size, so larger checkpoints are also more expensive to run per image, on top of carrying more parameters.

Train

from libreyolo import LibreYOLO model = LibreYOLO("LibrePICODETs.pt")model.train(    data="my-dataset.yaml",    epochs=300, batch=16, lr0=0.01,)

# imgsz is worth setting: the CLI defaults it to 640, while the s# checkpoint is native at 320.libreyolo train model=LibrePICODETs.pt data=my-dataset.yaml imgsz=320 epochs=300 batch=16 lr0=0.01

The loss components and the assigner follow the upstream recipe: VFL, DFL, GIoU and SimOTA, with classification-quality weighting and dynamic-IoU VFL targets. Inference is bit-equivalent to upstream on the same checkpoint.

What has not been checked, per train()'s own docstring: full-dataset convergence, multi-GPU behavior, and any augmentation beyond horizontal flip. The s checkpoint at its native 320 has also not reliably cleared LibreYOLO's accuracy floor on the 30-image, two-class fixture the library tests small fine-tunes with. That size is a better fit at full-COCO scale.

train() also accepts a pretrained argument, but the value is never read inside the method: training always continues from whatever weights the model was constructed with, so pretrained=False does not reinitialize the network. Leave imgsz unset in Python and it takes the loaded checkpoint's native resolution, 320 for s, 416 for m and 640 for l. The CLI always sends an imgsz, defaulting to 640, so set it there to match the checkpoint.

Left alone otherwise, the trainer runs 300 epochs with SGD at lr0=0.01, momentum 0.9, weight decay 4e-5 and a 1-epoch warmup on a cosine schedule. Horizontal flip is the only augmentation applied.

See training for datasets, augmentation, multi-GPU and loggers.

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("LibrePICODETs.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

libreyolo val model=LibrePICODETs.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. Running the graph in a bare runtime, with no LibreYOLO installed, is also supported, but then preprocessing and postprocessing are yours to write.

from libreyolo import LibreYOLO model = LibreYOLO("LibrePICODETs.pt")model.export(format="onnx", imgsz=320)model.export(format="tensorrt", imgsz=320, half=True)

libreyolo export model=LibrePICODETs.pt format=onnx imgsz=320libreyolo export model=LibrePICODETs.pt format=tensorrt imgsz=320 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("LibrePICODETs.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@misc{yu2021pppicodet,
      title={PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices},
      author={Guanghua Yu and Qinyao Chang and Wenyu Lv and Chang Xu and Cheng Cui and Wei Ji and Qingqing Dang and Kaipeng Deng and Guanzhong Wang and Yuning Du and Baohua Lai and Qiwen Liu and Xiaoguang Hu and Dianhai Yu and Yanjun Ma},
      year={2021},
      eprint={2111.00902},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}