DeiT

Install

DeiT 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. The -cls suffix in the filename is required and selects the classification task.

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreDeiTb-cls.pt")result = model(SAMPLE_IMAGE) print(result.probs.top1, result.probs.top1conf)print(result.probs.top5)

libreyolo predict model=LibreDeiTb-cls.pt source=https://raw.githubusercontent.com/LibreYOLO/libreyolo/release/libreyolo/assets/parkour.jpg

The returned Results object carries a probs tensor instead of boxes; top1 and top5 index the 1,000 ImageNet-1k classes and top1conf is the softmax score for the top prediction. Each size has a fixed input resolution from its positional embedding: preprocessing resizes and center-crops to it, and passing a different imgsz raises rather than silently resampling. See prediction for sources, streaming and result handling.

Validate

val() returns a dictionary with top-1 and top-5 accuracy, measured against a dataset laid out in the conventional train/<class>/ and val/<class>/ folder structure.

from libreyolo import LibreYOLO model = LibreYOLO("LibreDeiTb-cls.pt")metrics = model.val(data="my-dataset.yaml") print(metrics["metrics/accuracy_top1"])print(metrics["metrics/accuracy_top5"])

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

libreyolo export model=LibreDeiTb-cls.pt format=onnxlibreyolo export model=LibreDeiTb-cls.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("LibreDeiTb-cls.onnx")result = model(SAMPLE_IMAGE) print(result.probs.top1)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@InProceedings{pmlr-v139-touvron21a,
  title =     {Training data-efficient image transformers & distillation through attention},
  author =    {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
  booktitle = {International Conference on Machine Learning},
  pages =     {10347--10357},
  year =      {2021},
  volume =    {139},
  month =     {July}
}