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Sponsored by
Vmake Labs Video Enhancer
zeromodels / dino-vitb16
Model's Last Updated: August 27 2026
image-feature-extraction
Introduction of dino-vitb16
Model Details of dino-vitb16
See our collection for all versions of DINO.
Run DINO with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/dino-vitb16
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-vitb16
for
zeromodels
. One implementation runs unmodified on
TensorFlow / Torch / JAX
.
This is a
self-supervised backbone
(
DinoViTModel
), not a task head.
✨ Quick start
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-vitb16", include_normalization=False
)
processor = DinoImageProcessor.from_weights("zeromodels/dino-vitb16")
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_BACKENDbefore importing Keras / zeromodels. -
The processor normalizes; pair it with
include_normalization=False. To skip it, feed raw[0, 255]pixels and keep the defaultinclude_normalization=True. -
dino-resnet50was converted from torch.hubfacebookresearch/dino. - See DINO docs and Loading Weights .
-
Community / upstream weights:
DinoViTModel.from_weights("hf:facebook/dino-vitb16").
Special Thanks
A huge thank you to the Facebook AI Research DINO authors for creating and releasing these models.
License: Apache 2.0.
Runs of zeromodels dino-vitb16 on huggingface.co
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More Information About dino-vitb16 huggingface.co Model
Url of dino-vitb16
dino-vitb16 huggingface.co Url
Provider of dino-vitb16 huggingface.co
Other API from zeromodels
Updated:August 27 2026
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