---
library_name: pytorch
license: other
tags:
- qai-hub-models
- qualcomm
- android
pipeline_tag: depth-estimation
---

# DepthPro: Sharp monocular metric depth in less than a second, on-device

Apple DepthPro is a zero-shot monocular metric depth estimator that emits high-resolution, sharp depth maps from a single 1536x1536 RGB image. The architecture is a multi-scale Vision Transformer built on Dinov2 encoders with DPT-style fusion; alongside the depth map the model predicts a per-image horizontal field of view, which downstream calibration converts into a focal length in pixels. This recipe wraps Apple's HuggingFace checkpoint (`apple/DepthPro-hf`, ~952M parameters) at the model's native 1536x1536 input resolution.

This is based on the implementation of DepthPro found [here](https://github.com/apple/ml-depth-pro).
This is a standalone recipe compatible with the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models) CLI — it can be compiled and evaluated on real Snapdragon devices via [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.


## Setup
### 1. Install the package
Install the base package, fetch this recipe from Hugging Face, then use the
`qai-hub-models` CLI to install the recipe's dependencies:
```bash
# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install qai-hub-models
qai-hub-models register ashwmurt/depth_pro
qai-hub-models install depth_pro
```
`register` downloads the recipe and names it `depth_pro`, which is how every
command below refers to it.

### 2. Configure Qualcomm® AI Hub Workbench
Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.

With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://workbench.aihub.qualcomm.com/docs/) for more information.

## Run CLI Demo
Run the following simple CLI demo to verify the model is working end to end:

```bash
qai-hub-models demo depth_pro
```
More details on the CLI tool can be found with the `--help` option. See
[demo.py](demo.py) for sample usage of the model including pre/post processing
scripts.

By default, the demo will run locally in PyTorch. Pass `--eval-mode on-device` to run the model on a cloud-hosted target device.

## Export for on-device deployment
To run the model on Qualcomm® devices, you must export the model for use with an edge runtime such as
TensorFlow Lite, ONNX Runtime, or Qualcomm AI Engine Direct.
Use the following command to export the model:
```bash
qai-hub-models export depth_pro
```
Additional options are documented with the `--help` option.

## License
* The license for the original implementation of DepthPro can be found
  [here](https://huggingface.co/apple/DepthPro/blob/main/LICENSE).

## References
* [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073)
* [Source Model Implementation](https://github.com/apple/ml-depth-pro)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).