MLXDINOv3

Swift port of Meta's DINOv3 on MLX Swift.

DINOv3 is a self-supervised vision model from Meta that produces dense visual features useful for classification, segmentation, retrieval, etc. without fine-tuning. This package implements the architecture in MLX and validates outputs against a PyTorch reference.

Installation

Add to your Package.swift:

dependencies: [
    .package(url: "https://github.com/vincentamato/MLXDINOv3.git", from: "1.0.0")
]

import MLXDINOv3

Converting weights

The Convert target downloads a Hugging Face checkpoint and converts it to MLX format. Only ViT models are supported for now.

xcodebuild build -scheme Convert -destination platform=macOS -derivedDataPath .build/xcode && \
  .build/xcode/Build/Products/Release/Convert \
    facebook/dinov3-vits16-pretrain-lvd1689m \
    ./Models/dinov3-vits16-mlx

Usage

import AppKit
import MLX
import MLXDINOv3

let model = try loadPretrained(modelPath: "Models/dinov3-vits16-mlx")

let image = NSImage(contentsOfFile: "image.jpg")!
let processor = ImageProcessor()
let inputs = try processor(image)

let outputs = model(inputs)

print("Pooler output shape:", outputs.poolerOutput.shape)
print("Last hidden state shape:", outputs.lastHiddenState.shape)

Testing

Tests use xcodebuild because MLX depends on the Metal backend (swift test won't work). Before running tests, you need to convert the model into the test resources directory.

# Convert the test model (skip if already done)
xcodebuild build -scheme Convert -destination platform=macOS -derivedDataPath .build/xcode && \
  .build/xcode/Build/Products/Release/Convert \
    facebook/dinov3-vits16-pretrain-lvd1689m \
    Tests/MLXDINOv3Tests/Resources/Model

# Run tests
xcodebuild test -scheme MLXDINOv3Tests -destination platform=macOS

Tests download PyTorch reference outputs from Hugging Face and compare against them.

References

• DINOv3 paper
• facebookresearch/dinov3

License

MIT. See LICENSE.

Pretrained weights are under Meta's DINOv3 License.