LibreViTti-cls

Classic ViT-Tiny/16 image classifier (224px, ImageNet-1k, 1000 classes), repackaged for LibreYOLO. The model has 5.7 million parameters and is inference-only in LibreYOLO.

Source

Derived from timm model vit_tiny_patch16_224.augreg_in21k_ft_in1k at timm v1.0.28 (commit 8ef73809f622e0031bd7f4940265734aef8b9978). The exact source checkpoint is timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k at revision 7d3afdd0cf93ad84d986eb2d6bcc5812ebd0b106. The source and weights are Apache-2.0. Copyright (c) Ross Wightman and the timm contributors.

The architecture originates from Google Research's Apache-2.0 Vision Transformer. These AugReg weights use ImageNet-21k pretraining followed by ImageNet-1k fine-tuning.

Modifications

Learned parameters are unchanged. Conversion adds LibreYOLO checkpoint metadata (model_family, size, task, nc, names, and imgsz) only. LibreYOLO's native graph loads the state dict strictly and produces exactly the same pretrained logits as timm (max_abs_diff == 0). See weights/convert_vit_weights.py and docs/provenance/vit.md in the LibreYOLO source repository.

Source model.safetensors SHA-256: fecf81b492bd13ee7a5297cb74d1d417aac8bf7e1b7d96aed89c4691984587ed.

Usage

from libreyolo import LibreYOLO

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

License

Apache License 2.0. See LICENSE and NOTICE.