VGG

VGG is a convolutional image classifier built from uniform stacks of small 3x3 convolutions instead of larger filters. LibreYOLO ships the 16- and 19-layer sizes, plain and with batch normalization, for image classification.

Tasks: classify
Sizes: 16, 19, 16bn, 19bn at 224 px
Install: pip install libreyolo
Support tier: Inference only, since v. Predict, validate and export only. Training features do not apply.
Upstream: VGG by Visual Geometry Group, University of Oxford, BSD-3-Clause. Paper, source
Licenses: Code BSD-3-Clause, weights BSD-3-Clause. Commercial use

Install

VGG 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("LibreVGG16-cls.pt")result = model(SAMPLE_IMAGE, save=True) probs = result.probsprint(probs.top1, probs.top1conf)print(probs.top5, probs.top5conf)

CLI

libreyolo predict model=LibreVGG16-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. Prediction runs at a fixed 224px input and raises if you pass a different imgsz. See prediction for sources, streaming and result handling.

Variants

Four sizes: 16 and 19 convolutional layers, each with a plain and a batch-normalized variant. The shipped weights are torchvision's later from-scratch ImageNet training, not conversions of the Oxford group's original 2014 Caffe release. LibreYOLO ships this family inference-only: prediction, ImageNet-style top-1/top-5 validation and export are supported, and fine-tuning 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("LibreVGG16-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=LibreVGG16-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("LibreVGG16-cls.pt")model.export(format="onnx")model.export(format="tensorrt", half=True)

CLI

libreyolo export model=LibreVGG16-cls.pt format=onnxlibreyolo export model=LibreVGG16-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("LibreVGG16-cls.onnx")result = model(SAMPLE_IMAGE) print(result.probs.top1)

Checkpoints

Every published weight file for this family.

File	Input (px)	Weights license
classify
LibreVGG16-cls.pt	224	bsd-3-clause
LibreVGG19bn-cls.pt	224	bsd-3-clause
LibreVGG19-cls.pt	224	bsd-3-clause
LibreVGG16bn-cls.pt	224	bsd-3-clause

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: VGG, Visual Geometry Group, University of Oxford
Upstream license: BSD-3-Clause
Upstream source: github.com/pytorch/vision
LibreYOLO code: MIT
Weights: BSD-3-Clause, republished at huggingface.co/LibreYOLO
Interpretation: BSD-3-Clause is a permissive license, so these weights can be used in commercial and closed-source products. It asks you to keep the copyright notice, the list of conditions and the disclaimer with any copy you redistribute, and it forbids using the contributors' names to endorse a derived product without permission; it carries no explicit patent grant. LibreYOLO's code and the four shipped checkpoints (16, 19, 16-BN, 19-BN) are both derived from torchvision, not from the Oxford group's original 2014 Caffe release, which is a separate model under Creative Commons Attribution and is not what LibreYOLO redistributes. Torchvision itself notes that BSD-3-Clause redistribution of a pretrained checkpoint is an implied basis rather than a grant written for that specific checkpoint, and that pretrained-model terms can depend on the data a model was trained on; LibreYOLO repeats that caveat on each hosted weights repository.