README
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.
Similar repos
Linked in READMEs
README
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.