First model version

checkpoints

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-a

src/depth_pro.egg-info/PKG-INFO

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-Metadata-Version: 2.1
-Name: depth_pro
-Version: 0.1
-Summary: Inference/Network/Model code for Apple Depth Pro monocular depth estimation.
-Project-URL: Homepage, https://github.com/apple/ml-depth-pro
-Project-URL: Repository, https://github.com/apple/ml-depth-pro
-Description-Content-Type: text/markdown
-License-File: LICENSE
-Requires-Dist: torch
-Requires-Dist: torchvision
-Requires-Dist: timm
-Requires-Dist: numpy<2
-Requires-Dist: pillow_heif
-Requires-Dist: matplotlib
-
-## Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
-
-This software project accompanies the research paper:
-**Depth Pro: Sharp Monocular Metric Depth in Less Than a Second**, 
-*Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain, Marcel Santos, Yichao Zhou, Stephan R. Richter, and Vladlen Koltun*.
-
-![](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.
-
-
-The model 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.
-
-## Getting Started
-
-We recommend setting up a virtual environment. Using e.g. miniconda, the `depth_pro` package can be installed via:
-
-```bash
-conda create -n depth-pro -y python=3.9
-conda activate depth-pro
-
-pip install -e .
-```
-
-To download pretrained checkpoints follow the code snippet below:
-```bash
-source get_pretrained_models.sh   # Files will be downloaded to `checkpoints` directory.
-```
-
-### Running from commandline
-
-We provide a helper script to directly run the model on a single image:
-```bash
-# Run prediction on a single image:
-depth-pro-run -i ./data/example.jpg
-# Run `depth-pro-run -h` for available options.
-```
-
-### Running from python
-
-```python
-from PIL import Image
-import depth_pro
-
-# Load model and preprocessing transform
-model, transform = depth_pro.create_model_and_transforms()
-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) 
-
-Our boundary metrics can be found under `eval/boundary_metrics.py` and 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},
-}
-```
-
-## License
-This sample code is released under the [LICENSE](LICENSE) terms.
-
-The model weights are released under the [LICENSE](LICENSE) terms.
-
-## Acknowledgements
-
-Our codebase is built using multiple opensource contributions, please see [Acknowledgements](ACKNOWLEDGEMENTS.md) for more details.
-
-Please check the paper for a complete list of references and datasets used in this work.

src/depth_pro.egg-info/SOURCES.txt

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-ACKNOWLEDGEMENTS.md
-CODE_OF_CONDUCT.md
-CONTRIBUTING.md
-LICENSE
-README.md
-get_pretrained_models.sh
-pyproject.toml
-data/depth-pro-teaser.jpg
-data/example.jpg
-src/depth_pro/__init__.py
-src/depth_pro/depth_pro.py
-src/depth_pro/utils.py
-src/depth_pro.egg-info/PKG-INFO
-src/depth_pro.egg-info/SOURCES.txt
-src/depth_pro.egg-info/dependency_links.txt
-src/depth_pro.egg-info/entry_points.txt
-src/depth_pro.egg-info/requires.txt
-src/depth_pro.egg-info/top_level.txt
-src/depth_pro/cli/__init__.py
-src/depth_pro/cli/run.py
-src/depth_pro/eval/boundary_metrics.py
-src/depth_pro/eval/dis5k_sample_list.txt
-src/depth_pro/network/__init__.py
-src/depth_pro/network/decoder.py
-src/depth_pro/network/encoder.py
-src/depth_pro/network/fov.py
-src/depth_pro/network/vit.py
-src/depth_pro/network/vit_factory.py

src/depth_pro.egg-info/dependency_links.txt

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-

src/depth_pro.egg-info/entry_points.txt

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-[console_scripts]
-depth-pro-run = depth_pro.cli:run_main

src/depth_pro.egg-info/requires.txt

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-torch
-torchvision
-timm
-numpy<2
-pillow_heif
-matplotlib

src/depth_pro.egg-info/top_level.txt

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-depth_pro