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kerasformers/
dino-vits16

Name: dino-vits16
Availability: InStock

image-feature-extraction

facebook/dino-vits16

finetune

apache-2.0

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See our collection for all versions of DINO.

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

GitHub

zeromodels/dino-vits16

Paper: Emerging Properties in Self-Supervised Vision Transformers (arXiv:2104.14294) · HF Papers

DINO is self-supervised: a student and teacher match across crops of the same image with no labels. The resulting features are semantic for free. These checkpoints are backbones that return tokens / feature maps.

For more details on the model, please go to the upstream model card.

Pure-Keras 3 conversion of facebook/dino-vits16 for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is a self-supervised backbone (DinoViTModel), not a task head.

✨ Quick start

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from zeromodels.models.dino import DinoViTModel, DinoImageProcessor

# The processor resizes + ImageNet-normalizes, so build the model with
# include_normalization=False (it would otherwise normalize a second time).
model = DinoViTModel.from_weights(
    "zeromodels/dino-vits16", include_normalization=False
)
processor = DinoImageProcessor.from_weights("zeromodels/dino-vits16")

pixel_values = processor("your_image.jpg")["pixel_values"]
features = model(pixel_values, training=False)
print(pixel_values.shape, features.shape)

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

Variant	Hub	Backbone
`dino-vits16`	`zeromodels/dino-vits16`	ViT-S/16
`dino-vits8`	`zeromodels/dino-vits8`	ViT-S/8
`dino-vitb16`	`zeromodels/dino-vitb16`	ViT-B/16
`dino-vitb8`	`zeromodels/dino-vitb8`	ViT-B/8
`dino-resnet50`	`zeromodels/dino-resnet50`	ResNet-50

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.
dino-resnet50 was converted from torch.hub facebookresearch/dino.
See DINO docs and Loading Weights.
Community / upstream weights: DinoViTModel.from_weights("hf:facebook/dino-vits16").

Special Thanks

A huge thank you to the Facebook AI Research DINO authors for creating and releasing these models.

License: Apache 2.0.

kerasformers/dino-vits16