---
library_name: keras-hub
---
### Model Overview
Vision Transformer (ViT) model trained using the DINOv2 method.

**Reference**

- [Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)
- [Vision Transformers Need Registers](https://arxiv.org/abs/2309.16588)

DINOV2 offers a powerful, generalist visual backbone learned entirely from
unlabeled images as described in DINOv2: Learning Robust Visual Features
without Supervision

## Links

* [DINOv2 Quickstart Notebook] - coming soon
* [DINOv2 API Documentation] - coming soon
* [DINOv2 Beginner Guide] - coming soon
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)


## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras
```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.

## Presets

The following model checkpoints are provided by the Keras team. Weights have been ported from: https://huggingface.co. Full code examples for each are available below.

| Preset name                        | Parameters | Description                                                                                                                                                                                                                                                                                               |
|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| dinov2_small | 22.58M      | Vision Transformer (small-sized model) trained using DINOv2. |
| dinov2_base | 87.63M      | Vision Transformer (base-sized model) trained using DINOv2. |
| dinov2_large | 305.77M      | Vision Transformer (large-sized model) trained using DINOv2. |
| dinov2_giant | 1.13B     | Vision Transformer (giant-sized model) trained using DINOv2.|
| dinov2_with_registers_small | 22.58M      | Vision Transformer (small-sized model) trained using DINOv2, with registers. |
| dinov2_with_registers_base | 87.63M      | Vision Transformer (base-sized model) trained using DINOv2, with registers. |
| dinov2_with_registers_large | 305.77M      | Vision Transformer (large-sized model) trained using DINOv2, with registers. |
| dinov2_with_registers_giant | 1.13B     | Vision Transformer (giant-sized model) trained using DINOv2, with registers.|