iBims-1 Depth Dataset

iBims-1 (independent Benchmark images and matched scans) is a high-quality indoor benchmark for monocular depth estimation. It pairs RGB images with survey-grade laser-scanner ground truth and is designed to test sharp depth edges and planar surfaces.

Key Features

• High-quality depth ground truth captured with a survey-grade laser scanner.
• Covers real indoor scenes.
• Depth range up to approximately 10 m.
• Evaluation is performed on 100 images.
• Specifically designed to evaluate sharp depth edges and the quality of planar surfaces.

Role in YOLO26-Depth

iBims-1 is a zero-shot evaluation benchmark for the YOLO26-Depth family; the published models are not trained on it. Its focus on sharp depth discontinuities and planar regions makes it a precise test of depth-map quality beyond aggregate accuracy.

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 iBims-1 evaluation images by model size.

Evaluation

iBims-1 is not shipped with a bundled dataset YAML. It is evaluated through a dedicated evaluation script that loads the iBims-1 images and laser-scanner depth ground truth, applies the standard TTA and log-least-squares scale alignment, and reports the depth metrics.

Usage

iBims-1 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 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 iBims-1 images
    results = model.predict("path/to/ibims1/images")
    ```

=== "CLI"

    ```bash
    # Predict depth with a pretrained *.pt model
    yolo depth predict model=yolo26x-depth.pt source=path/to/ibims1/images
    ```

Pretrained Models

The YOLO26 depth family is evaluated zero-shot on the iBims-1 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 iBims-1 dataset in your research or development work, please cite the following paper:

!!! quote ""

=== "BibTeX"

    ```bibtex
    @inproceedings{koch2018ibims,
          title={Evaluation of CNN-based Single-Image Depth Estimation Methods},
          author={Koch, Tobias and Liebel, Lukas and Fraundorfer, Friedrich and K{\"o}rner, Marco},
          booktitle={Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
          year={2018}
    }
    ```

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