Link to this sectionDIODE Depth Dataset#

DIODE (Dense Indoor/Outdoor DEpth) is a real-world dataset with very high-quality dense depth ground truth captured by a FARO Focus survey-grade laser scanner. Uniquely, it covers both indoor and outdoor scenes with the same sensor, making it a high-precision bridge between short-range indoor and long-range outdoor depth for monocular depth estimation.

Link to this sectionKey Features#

• Very high-quality dense depth ground truth from a FARO Focus survey-grade laser scanner.
• Covers both indoor and outdoor scenes captured with the same sensor.
• Depth range spans short indoor distances to long outdoor distances, up to approximately 80 m in the Ultralytics mix.
• Dense, accurate per-pixel ground truth aligned to the RGB frames.
• Provides high-precision dense ground truth that bridges indoor and outdoor domains.

Link to this sectionDataset Structure#

The DIODE depth dataset is split into two subsets:

1. Train: 25,458 images with paired depth maps for training.
2. Val: 771 images with paired depth maps for validation during model training.

Each sample consists of one RGB image and one paired .npy float32 depth map storing per-pixel distances in meters, following the Ultralytics depth dataset format.

Link to this sectionRole in YOLO26-Depth#

DIODE is a training source in the Ultralytics YOLO26-Depth multi-dataset pretraining mix of roughly 2.19M image–depth pairs. It contributes high-precision dense ground truth that bridges indoor and outdoor domains within a single sensor, helping the model generalize across both short-range and long-range scenes. The resulting models are evaluated on the standard NYU, KITTI, Make3D, ETH3D, and iBims-1 benchmarks.

Link to this sectionDataset 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.

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# DIODE dataset for monocular depth estimation — real indoor + outdoor, FARO Focus survey-grade laser, depth up to ~80 m
# Documentation: https://docs.ultralytics.com/datasets/depth/diode
# Example usage: yolo depth train data=depth-diode.yaml model=yolo26n-depth.pt
# parent
# ├── ultralytics
# └── datasets
#     └── depth-diode  ← downloads here (84 GB archives, ~90 GB converted)
#         ├── images/{train,val}  # RGB images
#         └── depth/{train,val}   # paired *.npy, float32 meters (images/ -> depth/)
# If an interrupted download leaves a partial dataset, delete the depth-diode dir and re-run to rebuild it.

path: depth-diode # dataset root dir (relative to Ultralytics settings 'datasets_dir')
train: images/train # train images (relative to 'path') 25458 images
val: images/val # val images (relative to 'path') 771 images
max_depth: 80 # (m) maximum valid depth; GT beyond this is excluded from val metrics

nc: 1
names:
  0: depth

channels: 3

# Download script/URL (optional)
download: |
  import shutil
  from pathlib import Path

  import numpy as np

  from ultralytics.utils import TQDM
  from ultralytics.utils.downloads import download

  # Download and extract the official archives (train ~81 GB, val ~2.6 GB), then convert:
  # flatten <split>/<scene>/<scan>/*.png into images/<split>/ and save the paired *_depth.npy
  # (masked invalid -> 0, clipped at 80 m) as depth/<split>/*.npy
  dir = Path(yaml["path"])  # dataset root dir
  download([f"https://diode-dataset.s3.amazonaws.com/{s}.tar.gz" for s in ("train", "val")], dir=dir / "source", delete=True)
  for split in ("train", "val"):
      (dir / "images" / split).mkdir(parents=True, exist_ok=True)
      (dir / "depth" / split).mkdir(parents=True, exist_ok=True)
      for im in TQDM(sorted((dir / "source" / split).rglob("*.png")), desc=f"Converting {split}"):
          name = "_".join(im.relative_to(dir / "source").parts)  # train/indoors/scene/scan/x.png -> train_indoors_scene_scan_x.png
          depth = np.load(im.with_name(f"{im.stem}_depth.npy")).squeeze().astype(np.float32)
          depth[np.load(im.with_name(f"{im.stem}_depth_mask.npy")) == 0] = 0.0  # zero out invalid pixels
          np.save(dir / "depth" / split / f"{name[:-4]}.npy", depth.clip(max=80))
          im.replace(dir / "images" / split / name)
  shutil.rmtree(dir / "source")

Link to this sectionUsage#

To train a YOLO26n-depth model on the DIODE dataset with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-depth.pt")  # load a pretrained depth model (recommended for training)

# Train the model
results = model.train(data="depth-diode.yaml", epochs=100, imgsz=640)

Link to this sectionPretrained Models#

The YOLO26 depth family is trained on the broad multi-dataset depth pretraining mix that DIODE is part of. These models auto-download from the latest Ultralytics release, for example YOLO26x-depth from v8.4.0, and span a range of sizes for different accuracy and resource requirements.

Link to this sectionCitations and Acknowledgments#

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

@article{vasiljevic2019diode,
      title={DIODE: A Dense Indoor and Outdoor DEpth Dataset},
      author={Vasiljevic, Igor and Kolkin, Nick and Zhang, Shanyi and Luo, Ruotian and Wang, Haochen and Dai, Falcon Z. and Daniele, Andrea F. and Mostajabi, Mohammadreza and Basart, Steven and Walter, Matthew R. and Shakhnarovich, Gregory},
      journal={arXiv preprint arXiv:1908.00463},
      year={2019}
}

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