YOLOv4

Install

YOLOv4 needs no extra beyond the base package.

pip install libreyolo

Predict

This family is inference-only: train() raises NotImplementedError, so this page has no Train section. Predict, validate and export are all supported. Weights download from Hugging Face on first use and are cached locally.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreYOLO4b.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

libreyolo predict model=LibreYOLO4b.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 filters the confidence threshold and iou the NMS threshold, applied after each head's own scale_x_y center scaling. See prediction for sources, streaming and result handling.

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 validate on.

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLO4b.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/mAP50-95"])print(metrics["metrics/mAP50"])

libreyolo val model=LibreYOLO4b.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("LibreYOLO4b.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

libreyolo export model=LibreYOLO4b.pt format=onnxlibreyolo export model=LibreYOLO4b.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("LibreYOLO4b.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@misc{bochkovskiy2020yolov4,
      title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, 
      author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
      year={2020},
      eprint={2004.10934},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}