vit_small_patch16_224.dino-mlxim

How to use

pip install mlx-image

Here is how to use this model for feature extraction:

import mlx.core as mx
from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform

transform = ImageNetTransform(train=False, img_size=224)
x = transform(read_rgb("image.png"))
x = mx.expand_dims(x, 0)

model = create_model("vit_small_patch16_224.dinov3")
model.eval()

embeds = model(x, is_training=False)

You can also use the embeddings from the layer before the head:

import mlx.core as mx
from mlxim.model import create_model
from mlxim.io import read_rgb
from mlxim.transform import ImageNetTransform

transform = ImageNetTransform(train=False, img_size=224)
x = transform(read_rgb("image.png"))
x = mx.expand_dims(x, 0)

model = create_model("vit_small_patch16_224.dinov3", num_classes=0)
model.eval()

embeds = model(x, is_training=False)

Architecture

This model follows the ViT architecture with a patch size of 16. For a 224×224 image this results in 1 class token + 4 register tokens + 196 patch tokens = 201 tokens.

The model can accept larger images provided the image shapes are multiples of the patch size (16). If this condition is not met, the model will crop to the closest smaller multiple.

Key architectural features over DINOv2:

• RoPE: Rotary Position Embeddings for 2D images
• SwiGLU: Efficient SwiGLU feed-forward networks
• LayerScale: For improved training stability in deep transformers
• Register tokens: 4 additional register/storage tokens

Available model variants (mlx-image)

Evaluation results

Results on global and dense tasks (LVD-1689M pretraining)

Refer to the DINOv3 paper for full evaluation details and protocols.

Training data

The model was distilled from DINOv3 ViT-7B, which was pretrained on LVD-1689M — a curated dataset of 1,689 million images from public web sources.

Bias and limitations

DINOv3 delivers generally consistent performance across income categories on geographical fairness benchmarks, though a performance gap between low-income and high-income buckets remains. A relative difference is also observed between European and African regions. Fine-tuning may amplify these biases depending on the fine-tuning labels used.

Acknowledgements

Original model developed by Meta AI. See the blog post and paper. Weights ported to MLX by etornam45.