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Fix clarity issues in README #13

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Fix clarity issues in README #13

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12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -9,11 +9,11 @@ This software project accompanies the research paper:
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.
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:
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
Expand All @@ -27,13 +27,13 @@ To download pretrained checkpoints follow the code snippet below:
source get_pretrained_models.sh # Files will be downloaded to `checkpoints` directory.
```

### Running from commandline
### Running from command line

We provide a helper script to directly run the model on a single image:
```bash
# Run prediction on a single image:
# Run predictions on a single image:
depth-pro-run -i ./data/example.jpg
# Run `depth-pro-run -h` for available options.
# Run `depth-pro-run -h` to see available options.
```

### Running from python
Expand All @@ -59,7 +59,7 @@ 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:
Our boundary metrics can be found under `eval/boundary_metrics.py` and are used as follows:

```python
# for a depth-based dataset
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