| --- |
| license: apache-2.0 |
| tags: |
| - computer-vision |
| - feature-extraction |
| - vision-transformer |
| - onnx |
| - dinov2 |
| pipeline_tag: feature-extraction |
| --- |
| |
| # DINOv2 ONNX Weights |
|
|
| This repository contains the ONNX-optimized weights for **DINOv2**, a foundation model used for extracting global visual feature representations and embeddings from images. |
|
|
| By converting the original PyTorch model weights into the ONNX format, these files allow you to run fast feature-extraction inference on both CPU and GPU without needing to install the heavy PyTorch framework. |
|
|
| ## Available Files |
| * **`dinov2_vits14.onnx`**: ViT-Small backbone (384-dim embeddings), optimized for high throughput and minimal memory usage. |
| * **`dinov2_vitb14.onnx`**: ViT-Base backbone (768-dim embeddings), balanced descriptor capacity and speed. |
| * **`dinov2_vitl14.onnx`**: ViT-Large backbone (1024-dim embeddings), standard high-quality representation model. |
| * **`dinov2_vitg14.onnx` & `dinov2_vitg14.onnx.data`**: ViT-Giant backbone (1536-dim embeddings), maximum representation capacity model. |
| |
| --- |
| |
| ## How to Use |
| |
| The easiest way to load and use these files is through the **[spatialhub](https://github.com/spatialhub-ai/spatialhub)** Python library. |
| |
| ## Original Citation |
| If you use these models in academic work, please cite the original authors: |
| ```bibtex |
| @article{oquab2023dinov2, |
| title={DINOv2: Learning Robust Visual Features without Supervision}, |
| author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others}, |
| journal={arXiv preprint arXiv:2304.07193}, |
| year={2023} |
| } |
