apple/coreml-depth-anything-small

Depth Anything Core ML Models

Depth Anything model was introduced in the paper Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data by Lihe Yang et al. and first released in this repository.

Model description

Depth Anything leverages the DPT architecture with a DINOv2 backbone.

The model is trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation.

Depth Anything overview. Taken from the original paper.

Evaluation - Variants

Variant	Parameters	Size (MB)	Weight precision	Act. precision	abs-rel error	abs-rel reference
small-original (PyTorch)	24.8M	99.2	Float32	Float32
DepthAnythingSmallF32	24.8M	99.0	Float32	Float32	0.0073	small-original
DepthAnythingSmallF16	24.8M	45.8	Float16	Float16	0.0077	small-original

Evaluation - Inference time

The following results use the small-float16 variant.

Device	OS	Inference time (ms)	Dominant compute unit
iPhone 12 Pro Max	18.0	31.10	Neural Engine
iPhone 15 Pro Max	17.4	33.90	Neural Engine
MacBook Pro (M1 Max)	15.0	32.80	Neural Engine
MacBook Pro (M3 Max)	15.0	24.58	Neural Engine

Download

Install huggingface-cli

brew install huggingface-cli

To download one of the .mlpackage folders to the models directory:

huggingface-cli download \
  --local-dir models --local-dir-use-symlinks False \
  apple/coreml-depth-anything-small \
  --include "DepthAnythingSmallF16.mlpackage/*"

To download everything, skip the --include argument.

Integrate in Swift apps

The huggingface/coreml-examples repository contains sample Swift code for coreml-depth-anything-small and other models. See the instructions there to build the demo app, which shows how to use the model in your own Swift apps.