license: apache-2.0
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.
Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team.
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 |
|---|---|---|---|---|---|---|
| base-original (PyTorch) | 97.5M | 390 | Float32 | Float32 | ||
| small-original (PyTorch) | 24.8M | 99.2 | Float32 | Float32 | 0.1589 | base-original |
| base-float32 | 97.5M | 194.6 | Float32 | Float32 | 0.0056 | base-original |
| base-float16 | 97.5M | 194.6 | Float16 | Float16 | 0.0061 | base-original |
| small-float32 | 24.8M | 99.0 | Float32 | Float32 | 0.0073 | small-original |
| small-float16 | 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 14 | 17.5 | 160.59 | Neural Engine |
| iPhone 14 Pro Max | 17.5 | 119.33 | Neural Engine |
| iPhone 15 | 17.0 | 99.42 | Neural Engine |
| iPhone 15 Pro Max | 17.4 | 116.1 | Neural Engine |
| MacBook Pro (M1 Max) | 14.5 | 32.20 | GPU |
Download
Install huggingface-hub
pip install huggingface-hub
To download one of the .mlpackage folders to the models directory:
huggingface-cli download \
--local-dir models --local-dir-use-symlinks False \
coreml-projects/depth-anything \
--include "DepthAnythingSmallF16.mlpackage/*"
To download everything, skip the --include argument.