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Link to this sectionDepth8 Dataset#

Link to this sectionIntroduction#

The Ultralytics Depth8 dataset is a compact monocular depth estimation dataset with 8 images sampled from the SUN RGB-D dataset: 4 for training and 4 for validation, drawn from its Kinect v1 and Kinect v2 captures (two per sensor in each split) for dense, artifact-free ground-truth depth maps. It is designed for rapid testing, debugging, and experimentation with YOLO26 depth estimation models and training pipelines — the 1.3 MB archive auto-downloads on first use, so yolo depth train data=depth8.yaml starts training within seconds.

Note

Depth8 is for pipeline testing only, not benchmarking — its 8 images are far too few for meaningful depth metrics. Use the full NYU Depth V2 or SUN RGB-D validation sets for representative results.

Link to this sectionDataset Structure#

Depth8 follows the standard Ultralytics depth dataset layout: RGB images with paired .npy float32 depth maps in meters, matched by file stem.

depth8/
├── images/
│   ├── train/  # 4 images
│   └── val/    # 4 images
└── depth/
    ├── train/  # 4 float32 .npy depth maps
    └── val/    # 4 float32 .npy depth maps

Depth values are real indoor sensor captures ranging from roughly 0.5 m to 4 m, well within the ≤10 m range of the full SUN RGB-D dataset.

Link to this sectionDataset YAML#

The Depth8 dataset configuration is defined in a dataset YAML file, which specifies dataset paths, class names, and the download URL for the small packaged subset.

ultralytics/cfg/datasets/depth8.yaml
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Depth8 dataset (8 clean-label indoor images from SUN RGB-D Kinect v1/v2, 4 train / 4 val) by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/depth/depth8
# Example usage: yolo depth train data=depth8.yaml model=yolo26n-depth.pt
# parent
# ├── ultralytics
# └── datasets
#     └── depth8 ← downloads here (1.3 MB)
#         ├── images/{train,val}  # RGB images
#         └── depth/{train,val}   # paired *.npy, float32 meters (images/ -> depth/)

path: depth8 # dataset root dir (relative to Ultralytics settings 'datasets_dir')
train: images/train # train images (relative to 'path') 4 images
val: images/val # val images (relative to 'path') 4 images

nc: 1
names:
  0: depth

channels: 3

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/depth8.zip

Link to this sectionUsage#

To train a YOLO26n-depth model on the Depth8 dataset with an image size of 640, use the following examples. For a full list of training options, see the YOLO Training documentation.

Train Example
from ultralytics import YOLO

# Load a pretrained YOLO26n-depth model
model = YOLO("yolo26n-depth.pt")

# Train the model on Depth8
results = model.train(data="depth8.yaml", epochs=100, imgsz=640)

Link to this sectionCitations and Acknowledgments#

Depth8 is sampled from SUN RGB-D — see the full SUN RGB-D dataset page for license details.

If you use the SUN RGB-D dataset in your research or development work, please cite the following paper:

Quote
@inproceedings{song2015sunrgbd,
      title={SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite},
      author={Song, Shuran and Lichtenberg, Samuel P. and Xiao, Jianxiong},
      booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      year={2015}
}

Link to this sectionFAQ#

Link to this sectionWhat is the Ultralytics Depth8 dataset used for?#

The Ultralytics Depth8 dataset is designed for rapid testing and debugging of monocular depth estimation pipelines. With only 8 images (4 train, 4 val) in a 1.3 MB auto-downloading archive, it verifies the full train / val / predict cycle — paired depth-map loading, augmentation, loss computation, and metrics — in seconds, before scaling to a full dataset such as SUN RGB-D or NYU Depth V2.

Link to this sectionHow does Depth8 differ from the full SUN RGB-D dataset?#

Depth8 samples 8 images from SUN RGB-D's 9,245-train/1,090-val split, favoring Kinect v1/v2 captures with clean, dense depth maps. It uses the identical directory layout and .npy depth format, so a pipeline that runs on Depth8 runs unmodified on the full dataset — just point data= at depth-sunrgbd.yaml instead of depth8.yaml. Unlike the full dataset, Depth8 downloads in seconds and needs no conversion step.

Link to this sectionShould I use Depth8 for benchmarking?#

No. Depth8 is too small for meaningful model comparison and is intended for training and evaluation pipeline checks. Use the full NYU Depth V2 or SUN RGB-D validation sets when you need representative depth estimation metrics.

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