license: apache-2.0
library_name: libreyolo
pipeline_tag: image-classification
tags:
- image-classification
- vision-transformer
- vit
- imagenet
- libreyolo
datasets:
- imagenet-1k
LibreViTb-cls
Classic ViT-Base/16 image classifier (224px, ImageNet-1k, 1000 classes), repackaged for LibreYOLO. The model has 86.6 million parameters and is inference-only in LibreYOLO.
Source
Derived from timm model
vit_base_patch16_224.augreg2_in21k_ft_in1k at timm v1.0.28 (commit
8ef73809f622e0031bd7f4940265734aef8b9978). The exact source checkpoint is
timm/vit_base_patch16_224.augreg2_in21k_ft_in1k
at revision 063c6c38a5d8510b2e57df480445e94b231dad2c. 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 AugReg2 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:
32aa17d6e17b43500f531d5f6dc9bc93e56ed8841b8a75682e1bb295d722405b.
Usage
from libreyolo import LibreYOLO
model = LibreYOLO("LibreViTb-cls.pt")
result = model.predict("image.jpg")
print(result.probs.top1, result.probs.top5)