LibreAlexNetb-cls

AlexNet image classification weights repackaged for LibreYOLO. This is torchvision's single-tower, 64-channel-stem variant: no local response normalization and no grouped convolutions. It is not the two-GPU 2012 graph.

from libreyolo import LibreYOLO

model = LibreYOLO("LibreAlexNetb-cls.pt")
result = model.predict("image.jpg")
print(result.probs.top1, result.probs.top5)

Source

Derived from pytorch/vision at commit 336d36e8db990a905498c73933e35231876e28bc. Copyright (c) Soumith Chintala 2016 and torchvision contributors. The source implementation is BSD-3-Clause.

Official checkpoint: alexnet-owt-7be5be79.pth
Official checkpoint SHA-256: 7be5be791159472b1fbf3c69796f7cb30dca7ad8466c2df70058c37116cdee02
Official checkpoint bytes: 244408911
Published ImageNet-1K accuracy: 56.522% top-1, 79.066% top-5.

Modifications

LibreYOLO metadata and the canonical filename were added. Learned tensors and state-dict keys are unchanged. The native graph strict-loads the official state dict and produces bit-identical logits (max_abs_diff == 0.0).

Converted checkpoint SHA-256: 95f6996b7b4c5526e7e47ad99cf78b2a3643baa3ba1d4107ab840a05e73d1f5e
Converted checkpoint bytes: 244431825.

The converter and parity tests are in the LibreYOLO source repository.

License

The checkpoint publisher did not attach a separate per-object license file. This mirror applies the releasing project's BSD-3-Clause license on an implied, not publisher-confirmed, basis. Torchvision warns that pretrained models may have their own licenses or terms derived from training data and that users must determine whether they have permission for their use case. The weights were trained on ImageNet-1K; ImageNet's dataset and image-source terms remain the downstream user's responsibility. See LICENSE and NOTICE.