---
comments: true
description: Explore the Make3D outdoor benchmark for monocular depth estimation. Learn about its structure, usage, pretrained models, and role as a YOLO26-Depth zero-shot generalization benchmark.
keywords: Ultralytics, YOLO, depth estimation, Make3D, outdoor depth, laser scanner, monocular depth, out-of-distribution benchmark
---

# Make3D Depth Dataset

[Make3D](http://make3d.cs.cornell.edu/data.html) is a classic outdoor benchmark for [monocular depth estimation](index.md). It contains images of campus scenes paired with depth ground truth captured by a custom 3D laser scanner, and is widely used to probe out-of-distribution generalization.

## Key Features

- Depth ground truth captured with a custom 3D laser scanner.
- Covers real **outdoor** campus scenes.
- Depth range up to approximately 70 m.
- Evaluation is performed on the standard test set of 134 images.
- A classic out-of-distribution generalization benchmark.

## Role in YOLO26-Depth

Make3D is a **zero-shot evaluation benchmark** for the YOLO26-Depth family; the published models are not trained on it. As an out-of-distribution outdoor set, it is the hardest benchmark for all models, and absolute `delta1` values are low across the board, which is expected for this dataset.

Evaluation uses multi-scale and horizontal-flip test-time augmentation (TTA), followed by log-least-squares scale alignment between the predicted and ground-truth depth maps before metrics are computed.

## Results

The table below reports the `delta1` accuracy (percentage of pixels within a 1.25× threshold, higher is better) on the Make3D test set by model size.

| Model         | delta1 |
| ------------- | ------ |
| YOLO26n-depth | 0.307  |
| YOLO26s-depth | 0.311  |
| YOLO26m-depth | 0.293  |
| YOLO26l-depth | 0.297  |
| YOLO26x-depth | 0.299  |

## Evaluation

Make3D is not shipped with a bundled dataset YAML. It is evaluated through a dedicated evaluation script that loads the Make3D images and laser-scanner depth ground truth, applies the standard TTA and log-least-squares scale alignment, and reports the depth metrics.

## Usage

Make3D is an external benchmark, so models are typically run with `predict` on its images. For a comprehensive list of available arguments, refer to the model [Prediction](../../modes/predict.md) page.

!!! example "Predict Example"

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolo26x-depth.pt")  # load a pretrained depth model

        # Predict depth on Make3D images
        results = model.predict("path/to/make3d/images")
        ```

    === "CLI"

        ```bash
        # Predict depth with a pretrained *.pt model
        yolo depth predict model=yolo26x-depth.pt source=path/to/make3d/images
        ```

## Pretrained Models

The YOLO26 depth family is evaluated zero-shot on the Make3D benchmark. These models auto-download from the latest Ultralytics release, for example [YOLO26x-depth](https://platform.ultralytics.com/ultralytics/yolo26/yolo26x-depth) from v8.4.0, and span a range of sizes (yolo26n/s/m/l/x-depth) for different accuracy and resource requirements.

## Citations and Acknowledgments

If you use the Make3D dataset in your research or development work, please cite the following paper:

!!! quote ""

    === "BibTeX"

        ```bibtex
        @article{saxena2009make3d,
              title={Make3D: Learning 3D Scene Structure from a Single Still Image},
              author={Saxena, Ashutosh and Sun, Min and Ng, Andrew Y.},
              journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
              year={2009}
        }
        ```

We would like to acknowledge the authors for creating and maintaining this valuable resource for the computer vision community.
