YOLOX

Install

YOLOX 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("LibreYOLOXs.pt")result = model(SAMPLE_IMAGE, save=True) for box in result.boxes:    print(box.cls, box.conf, box.xyxy)

libreyolo predict model=LibreYOLOXs.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 applied across the three decoupled prediction scales. See prediction for sources, streaming and result handling.

Variants

Six sizes share the same CSP backbone and PAFPN neck. The two smallest, n and t, run at a smaller fixed input resolution than the other four; the benchmark table below carries the exact figure for each.

Train

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLOXs.pt")model.train(data="my-dataset.yaml", epochs=300, imgsz=640, batch=16, lr0=0.01)

libreyolo train model=LibreYOLOXs.pt data=my-dataset.yaml \  epochs=300 imgsz=640 batch=16 lr0=0.01

Left alone, the trainer runs 300 epochs at lr0=0.01 with SGD momentum 0.9, a 5-epoch warmup and mosaic and mixup augmentation switched off for the final 15 epochs. 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.

imgsz defaults to a fixed value in the base training config, not to the loaded checkpoint's native resolution. That affects the n and t checkpoints specifically: continuing to train either one without setting imgsz explicitly switches it up to the larger default rather than the smaller size it was published at.

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

libreyolo val model=LibreYOLOXs.pt data=my-dataset.yaml

# The bundled COCO yaml carries an embedded download script, so it# needs explicit permission unless the dataset is already local.libreyolo val model=LibreYOLOXn.pt data=coco.yaml imgsz=416 \  allow_download_scripts=True

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. A CoreML export can bake NMS into the graph with nms=True; YOLOX and YOLOv9 are the only two families that flag currently accepts.

from libreyolo import LibreYOLO model = LibreYOLO("LibreYOLOXs.pt")model.export(format="onnx", imgsz=640)model.export(format="tensorrt", imgsz=640, half=True)

libreyolo export model=LibreYOLOXs.pt format=onnx imgsz=640libreyolo export model=LibreYOLOXs.pt format=tensorrt imgsz=640 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("LibreYOLOXs.onnx")result = model(SAMPLE_IMAGE) print(result.boxes.xyxy)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}