NYU Depth V2 Depth Dataset

NYU Depth V2 is the standard indoor benchmark for monocular depth estimation. It consists of RGB-D video sequences of a wide variety of indoor scenes recorded with a Microsoft Kinect v1. It is the primary benchmark used to report YOLO26-Depth accuracy.

Key Features

• Captured with a Microsoft Kinect v1 RGB-D sensor.
• Covers a wide variety of real indoor scenes (homes, offices, classrooms, and similar spaces).
• Depth range up to approximately 10 m, typical of consumer indoor RGB-D capture.
• Evaluation is performed on the standard Eigen test split of 654 images.
• The primary benchmark for reporting monocular depth estimation accuracy.

Role in YOLO26-Depth

NYU Depth V2 is the primary zero-shot evaluation benchmark for the YOLO26-Depth family, and the headline metrics on the depth task page are reported on it. The published YOLO26-Depth models are not trained on NYU; although the dataset includes a train split, it is left unused and only held-out test results are reported.

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 NYU Depth V2 Eigen test split by model size.

Dataset YAML

A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information.

!!! example "ultralytics/cfg/datasets/nyu-depth.yaml"

```yaml
--8<-- "ultralytics/cfg/datasets/nyu-depth.yaml"
```

Usage

To evaluate a YOLO26-Depth model on the NYU Depth V2 benchmark, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Validation page.

!!! example "Validation Example"

=== "Python"

    ```python
    from ultralytics import YOLO

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

    # Evaluate on the NYU Depth V2 benchmark
    results = model.val(data="nyu-depth.yaml")
    ```

=== "CLI"

    ```bash
    # Evaluate a pretrained *.pt model
    yolo depth val data=nyu-depth.yaml model=yolo26x-depth.pt
    ```

Pretrained Models

The YOLO26 depth family is evaluated zero-shot on the NYU Depth V2 benchmark. These models auto-download from the latest Ultralytics release, for example 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 NYU Depth V2 dataset in your research or development work, please cite the following paper:

!!! quote ""

=== "BibTeX"

    ```bibtex
    @inproceedings{silberman2012indoor,
          title={Indoor Segmentation and Support Inference from RGBD Images},
          author={Silberman, Nathan and Hoiem, Derek and Kohli, Pushmeet and Fergus, Rob},
          booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
          year={2012}
    }
    ```

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