| --- |
| license: other |
| license_name: apple-sample-code-license |
| license_link: LICENSE |
| library_name: mlx |
| pipeline_tag: depth-estimation |
| base_model: apple/DepthPro |
| tags: |
| - mlx |
| - depth-estimation |
| - monocular-depth |
| - metric-depth |
| - apple-silicon |
| --- |
| |
| # Depth Pro — MLX |
|
|
| [Apple Depth Pro](https://github.com/apple/ml-depth-pro) (sharp monocular **metric** depth from a single image) converted to [MLX](https://github.com/ml-explore/mlx) for Apple Silicon. |
|
|
| Code, CLI and 3D photo pipeline: **[github.com/ntoantoan/depthpro-mlx](https://github.com/ntoantoan/depthpro-mlx)** |
|
|
| ## Usage |
|
|
| ```bash |
| pip install "depthpro-mlx[inpaint]" |
| depthpro-mlx photo.jpg --ply # depth.png, pointcloud.ply, 3d.mp4 |
| ``` |
|
|
| ```python |
| import numpy as np |
| from PIL import Image |
| from depthpro_mlx import load_model, predict |
| |
| model = load_model() # downloads these weights |
| image = np.array(Image.open("photo.jpg").convert("RGB")) |
| out = predict(model, image) # f_px=... if you know the focal length |
| out["depth"] # (H, W) depth in meters |
| out["focallength_px"] # estimated focal length |
| ``` |
|
|
| ## Files |
|
|
| | File | Description | |
| |---|---| |
| | `depth_pro.safetensors` | All 952M parameters in float16, NHWC conv layout | |
|
|
| ## Accuracy and speed |
|
|
| Parity against the official PyTorch implementation on the reference image (3024×2268): |
|
|
| | | Time (M4 Pro) | Peak memory | AbsRel vs. PyTorch fp32 | |
| |---|---|---|---| |
| | PyTorch MPS fp16 | 3.76 s | 7.0 GB | – | |
| | **MLX fp16** | **3.06 s** | 7.5 GB | 6.2e-4 | |
| | MLX fp32 | 4.17 s | 15.0 GB | 1.7e-6 | |
|
|
| ## License |
|
|
| These weights are a format conversion of Apple's released Depth Pro weights and are redistributed under Apple's original license terms (see `LICENSE`). That license is not OSI-approved: Apple grants a personal, non-exclusive license to use, modify and redistribute, and does not explicitly address commercial use, so check the terms before shipping the weights in a product. This conversion is not affiliated with or endorsed by Apple. |
|
|
| ```bibtex |
| @inproceedings{Bochkovskii2024:arxiv, |
| author = {Aleksei Bochkovskii and Ama\"{e}l Delaunoy and Hugo Germain and Marcel Santos and |
| Yichao Zhou and Stephan R. Richter and Vladlen Koltun}, |
| title = {Depth Pro: Sharp Monocular Metric Depth in Less Than a Second}, |
| booktitle = {International Conference on Learning Representations}, |
| year = {2025}, |
| url = {https://arxiv.org/abs/2410.02073}, |
| } |
| ``` |
|
|
