---
license: apple-ascl
pipeline_tag: depth-estimation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---

# Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

![Depth Pro Demo Image](https://github.com/apple/ml-depth-pro/raw/main/data/depth-pro-teaser.jpg)

We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details. The predictions are metric, with absolute scale, without relying on the availability of metadata such as camera intrinsics. And the model is fast, producing a 2.25-megapixel depth map in 0.3 seconds on a standard GPU. These characteristics are enabled by a number of technical contributions, including an efficient multi-scale vision transformer for dense prediction, a training protocol that combines real and synthetic datasets to achieve high metric accuracy alongside fine boundary tracing, dedicated evaluation metrics for boundary accuracy in estimated depth maps, and state-of-the-art focal length estimation from a single image.

Depth Pro was introduced in **[Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://arxiv.org/abs/2410.02073)**, by *Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, and Vladlen Koltun*.

The checkpoint in this repository is a reference implementation, which has been re-trained. Its performance is close to the model reported in the paper but does not match it exactly.

## How to Use

Please, follow the steps in the [code repository](https://github.com/apple/ml-depth-pro) to set up your environment. Then you can:

### Running from Python

```python
from huggingface_hub import PyTorchModelHubMixin
from depth_pro import create_model_and_transforms, load_rgb
from depth_pro.depth_pro import (create_backbone_model, load_monodepth_weights,
                                 DepthPro, DepthProEncoder, MultiresConvDecoder)
import depth_pro
from torchvision.transforms import Compose, Normalize, ToTensor


class DepthProWrapper(DepthPro, PyTorchModelHubMixin):
    """Depth Pro network."""

    def __init__(
        self,
        patch_encoder_preset: str,
        image_encoder_preset: str,
        decoder_features: str,
        fov_encoder_preset: str,
        use_fov_head: bool = True,
        **kwargs,
    ):
        """Initialize Depth Pro."""

        patch_encoder, patch_encoder_config = create_backbone_model(
            preset=patch_encoder_preset
        )
        image_encoder, _ = create_backbone_model(
            preset=image_encoder_preset
        )

        fov_encoder = None
        if use_fov_head and fov_encoder_preset is not None:
            fov_encoder, _ = create_backbone_model(preset=fov_encoder_preset)

        dims_encoder = patch_encoder_config.encoder_feature_dims
        hook_block_ids = patch_encoder_config.encoder_feature_layer_ids
        encoder = DepthProEncoder(
            dims_encoder=dims_encoder,
            patch_encoder=patch_encoder,
            image_encoder=image_encoder,
            hook_block_ids=hook_block_ids,
            decoder_features=decoder_features,
        )
        decoder = MultiresConvDecoder(
            dims_encoder=[encoder.dims_encoder[0]] + list(encoder.dims_encoder),
            dim_decoder=decoder_features,
        )

        super().__init__(
            encoder=encoder,
            decoder=decoder,
            last_dims=(32, 1),
            use_fov_head=use_fov_head,
            fov_encoder=fov_encoder,
        )


# Load model and preprocessing transform
model = DepthProWrapper.from_pretrained("apple/DepthPro-mixin")
transform = Compose(
        [
            ToTensor(),
            Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
        ]
    )


model.eval()

# Load and preprocess an image.
image, _, f_px = depth_pro.load_rgb(image_path)
image = transform(image)

# Run inference.
prediction = model.infer(image, f_px=f_px)
depth = prediction["depth"]  # Depth in [m].
focallength_px = prediction["focallength_px"]  # Focal length in pixels.
```

### Evaluation (boundary metrics) 

Boundary metrics are implemented in `eval/boundary_metrics.py` and can be used as follows:

```python
# for a depth-based dataset
boundary_f1 = SI_boundary_F1(predicted_depth, target_depth)

# for a mask-based dataset (image matting / segmentation) 
boundary_recall = SI_boundary_Recall(predicted_depth, target_mask)
```


## Citation

If you find our work useful, please cite the following paper:

```bibtex
@article{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},
  journal    = {arXiv},
  year       = {2024},
}
```

## Acknowledgements

Our codebase is built using multiple opensource contributions, please see [Acknowledgements](https://github.com/apple/ml-depth-pro/blob/main/ACKNOWLEDGEMENTS.md) for more details.

Please check the paper for a complete list of references and datasets used in this work.