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