| --- |
| 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](https://github.com/LibreYOLO/libreyolo). The model |
| has 86.6 million parameters and is inference-only in LibreYOLO. |
|
|
| ## Source |
|
|
| Derived from [timm](https://github.com/huggingface/pytorch-image-models) 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`](https://huggingface.co/timm/vit_base_patch16_224.augreg2_in21k_ft_in1k/tree/063c6c38a5d8510b2e57df480445e94b231dad2c) |
| 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](https://github.com/google-research/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](https://github.com/LibreYOLO/libreyolo). |
|
|
| Source `model.safetensors` SHA-256: |
| `32aa17d6e17b43500f531d5f6dc9bc93e56ed8841b8a75682e1bb295d722405b`. |
|
|
| ## Usage |
|
|
| ```python |
| from libreyolo import LibreYOLO |
| |
| model = LibreYOLO("LibreViTb-cls.pt") |
| result = model.predict("image.jpg") |
| print(result.probs.top1, result.probs.top5) |
| ``` |
|
|
| ## License |
|
|
| Apache License 2.0. See [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE). |
|
|
