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

# TartanAir dataset for monocular depth estimation — synthetic indoor + outdoor (AirSim), dense depth up to ~80 m
# Documentation: https://docs.ultralytics.com/datasets/depth/tartanair
# Example usage: yolo depth train data=depth-tartanair.yaml model=yolo26n-depth.pt
# No autodownload — obtain the source data (see docs) and arrange it as below.
# parent
# ├── ultralytics
# └── datasets
#     └── depth-tartanair
#         ├── images/{train,val}  # RGB images
#         └── depth/{train,val}   # paired 16-bit *.png depth maps (images/ -> depth/)

path: depth-tartanair # dataset root dir (relative to Ultralytics settings 'datasets_dir')
train: images/train # train images (relative to 'path') 55660 images
val: images/val # val images (relative to 'path') 5810 images
max_depth: 80 # (m) maximum valid depth; GT beyond this is excluded from val metrics

nc: 1
names:
  0: depth

channels: 3
depth_scale: 256 # PNG value 256 = 1 meter; represents the 80 m outdoor range
