ViT

The classic Vision Transformer: a pure transformer applied to fixed-size image patches, with a learned class token and no convolutions. LibreYOLO ships four AugReg-pretrained sizes for image classification.

Tasks: classify
Sizes: ti, s, b, l at 224 px
Install: pip install libreyolo
Support tier: Inference only, since v. Predict, validate and export only. Training features do not apply.
Upstream: ViT by Google Research, Apache-2.0. Paper, source
Licenses: Code Apache-2.0, weights Apache-2.0. Commercial use

Install

ViT needs no optional extra. Everything it imports is in the base install.

bash

pip install libreyolo

Predict

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

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE model = LibreYOLO("LibreViTti-cls.pt")result = model(SAMPLE_IMAGE, save=True) probs = result.probsprint(probs.top1, probs.top1conf)print(probs.top5, probs.top5conf)

CLI

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

A classifier returns result.probs instead of result.boxes: top1 and top5 give class indices, top1conf and top5conf give their confidences. Preprocessing resizes and center-crops to a fixed 224px input, using timm's AugReg evaluation recipe: bicubic interpolation at a 0.9 crop fraction. See prediction for sources, streaming and result handling.

Variants

Four sizes, tiny through large, sharing one fixed 224px, patch-16 graph and differing in embedding width and transformer depth. LibreYOLO ships this family inference-only: prediction, ImageNet-style top-1/top-5 validation and export are supported, and the AugReg fine-tuning recipe is not implemented.

Validate

val() runs against an ImageFolder-style split (a directory with train/ and val/ subfolders, one folder per class) and returns top-1 and top-5 accuracy.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreViTti-cls.pt") # data is a directory root with train/ and val/ class-folder splits# (ImageFolder layout), not a dataset YAML.metrics = model.val(data="imagenet-1k/") print(metrics["metrics/accuracy_top1"])print(metrics["metrics/accuracy_top5"])

CLI

libreyolo val model=LibreViTti-cls.pt data=imagenet-1k/

Export

Task	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
classify	classify to ONNX: supported.	classify to TorchScript: supported.	classify to ExecuTorch: supported.	classify to TensorRT: supported.	classify to OpenVINO: supported.	classify to Paddle: not supported	classify to MNN: not supported	classify to RKNN: not supported	classify to ncnn: supported.	classify to TFLite: not supported	classify to CoreML: not supported	classify to Core AI: not supported

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 and the extras a few of them add.

Python

from libreyolo import LibreYOLO model = LibreYOLO("LibreViTti-cls.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

CLI

libreyolo export model=LibreViTti-cls.pt format=onnxlibreyolo export model=LibreViTti-cls.pt format=tensorrt half=True

Use the exported file

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("LibreViTti-cls.onnx")result = model(SAMPLE_IMAGE) print(result.probs.top1)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
classify
LibreViTti-cls.pt	224	apache-2.0
LibreViTs-cls.pt	224	apache-2.0
LibreViTb-cls.pt	224	apache-2.0
LibreViTl-cls.pt	224	apache-2.0

Every file above exists in the LibreYOLO org today and downloads on first use.

Licensing

Check the license on the Hugging Face repository of the specific weights you download. Every checkpoint in the LibreYOLO org carries one, and they are not always the same across a family. That repository is the authoritative source; the summary below describes what applied when this page was last verified.

This is a description of the licenses involved, not legal advice. If the answer matters commercially, read the licenses yourself and take your own counsel.

Original work: ViT, Google Research
Upstream license: Apache-2.0
Upstream source: github.com/google-research/vision_transformer
LibreYOLO code: MIT
Weights: Apache-2.0, republished at huggingface.co/LibreYOLO
Interpretation: Apache-2.0 is a permissive license, so these weights can be used in commercial and closed-source products. It asks you to keep its license text and attribution notices with any copy of the weights you redistribute, and it grants a patent license. It places no obligation on your own application code. LibreYOLO's runtime code for this family is a derived port of the Apache-2.0 timm Vision Transformer implementation (Ross Wightman, huggingface/pytorch-image-models), kept checkpoint-compatible with the shipped tensors. The four AugReg checkpoints themselves are timm's Apache-2.0 conversion of Google Research's own AugReg pretraining, so the code and the weights carry the same permissive terms end to end.

Citation

@article{dosovitskiy2020vit,
  title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and  Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
  journal={ICLR},
  year={2021}
}

@article{steiner2021augreg,
  title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},
  author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
  journal={arXiv preprint arXiv:2106.10270},
  year={2021}
}

Copied from the authors' citation block at github.com/google-research/vision_transformer#bibtex.