VGG-16 image classifier (224px, ImageNet-1k, 1,000 classes), repackaged for
LibreYOLO. 138.4M parameters.
Derived from
pytorch/vision at commit
10f68dbd78b9aa5cab9328f3b2e99cfb0b608122, weight enum
VGG16_Weights.IMAGENET1K_V1, official file
vgg16-397923af.pth.
Copyright (c) Soumith Chintala 2016. The releasing project's code is licensed
under the BSD 3-Clause License.
The checkpoint object has no separate publisher-provided license file. This
repository applies BSD-3-Clause on the explicitly disclosed basis implied by
the releasing project, not as a confirmed checkpoint-specific grant.
Torchvision warns that pretrained-model terms can depend on training data and
users must determine permission for their use case. ImageNet is a separate
dataset and is not distributed here.
Learned parameters are unchanged. The checkpoint is metadata-wrapped into the
LibreYOLO format (
model_family,
task,
nc,
names, and schema fields).
LibreYOLO's native VGG graph retains the official module layout and produces
bit-exact logits (
max_abs_diff == 0). See
weights/convert_vgg_weights.py in the
LibreYOLO source repository.
1from libreyolo import LibreYOLO
2
3model = LibreYOLO("LibreVGG16-cls.pt")
4result = model.predict("image.jpg")[0]
5print(result.probs.top1, result.probs.top5)
BSD 3-Clause License, applied to this checkpoint on the disclosed implied basis
above. See
LICENSE and
NOTICE.
