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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},
}
```
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