LibreDeiTt-cls

Plain DeiT-Tiny patch-16 image classifier (fixed 224px input, ImageNet-1k, 1000 classes), packaged for LibreYOLO. It has 5.7M parameters.

Source

The unchanged learned tensors come from timm/deit_tiny_patch16_224.fb_in1k at revision 80e968688553f219e4a86f940ed945a23709c16f. The source model.safetensors SHA-256 is 21d4764d94f6c3ffdb6da3581115a0a1ee2d505537d96883b540e54766407c9e. That repository declares the checkpoint under Apache License 2.0.

The native graph follows huggingface/pytorch-image-models commit e98c05a5a15e81188ec62dd5380b8f5c3251075a (Apache-2.0). The original DeiT architecture is from facebookresearch/deit commit 7e160fe43f0252d17191b71cbb5826254114ea5b (Apache-2.0).

Modifications

Learned parameters and state-dict keys are unchanged. Conversion only adds LibreYOLO v1.0 checkpoint metadata and canonical ImageNet-1k class names. The native graph strict-loads the source state dictionary and produces bit-identical eager logits (max_abs_diff == 0). See weights/convert_deit_weights.py and docs/provenance/deit.md in the LibreYOLO source repository.

This museum model is inference-only. Distillation-token and 384px variants are not included.

Usage

from libreyolo import LibreYOLO

model = LibreYOLO("LibreDeiTt-cls.pt")  # downloads this checkpoint once
result = model.predict("image.jpg")
print(result.names[result.probs.top1], result.probs.top5)

License

Apache License 2.0. See LICENSE and NOTICE.