dinov2-giant

See our collection for all versions of DINOv2.

Run DINOv2 with Keras 3: JAX, PyTorch, or TensorFlow

zeromodels/dinov2-giant

Paper: DINOv2: Learning Robust Visual Features without Supervision (arXiv:2304.07193) · HF Papers DINOv2 scales self-supervised ViT pretraining for strong transferable visual features without labels. These checkpoints are backbones that return patch tokens for downstream heads. The giant (ViT-g/14) is the largest DINOv2 backbone and uses a SwiGLU FFN in place of the GELU MLP. For more details on the model, please go to the upstream model card. Pure-Keras 3 conversion of facebook/dinov2-giant for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX. This is a self-supervised backbone (DinoV2Model), not a task head.

✨ Quick start

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
from zeromodels.models.dino_v2 import DinoV2Model, DinoV2ImageProcessor
# The processor resizes + ImageNet-normalizes, so build the model with
# include_normalization=False (it would otherwise normalize a second time).
model = DinoV2Model.from_weights(
    "zeromodels/dinov2-giant", include_normalization=False
)
processor = DinoV2ImageProcessor.from_weights("zeromodels/dinov2-giant")
pixel_values = processor("your_image.jpg")["pixel_values"]
features = model(pixel_values, training=False)
print(pixel_values.shape, features.shape)

Load any DINOv2 variant the same way with from_weights("zeromodels/<variant>"):

Tips

• Set KERAS_BACKEND before importing Keras / zeromodels.
• The processor normalizes; pair it with include_normalization=False. To skip it, feed raw [0, 255] pixels and keep the default include_normalization=True.
• See DINOv2 docs and Loading Weights.
• Community / upstream weights: DinoV2Model.from_weights("hf:facebook/dinov2-giant").

Special Thanks

A huge thank you to the Facebook AI Research DINOv2 authors for creating and releasing these models. License: Apache 2.0.