data.yaml

# UAVid dataset configuration for Ultralytics YOLO semantic segmentation
#
# Format: https://docs.ultralytics.com/datasets/semantic/
# Masks:  single-channel PNG, pixel value = class index (0-7). 255 remains reserved
#         for genuinely unrecognized colours (e.g. corrupted/anti-aliased pixels) —
#         all 8 defined UAVid classes, including Clutter, are valid and used in both
#         training and evaluation, per the original UAVid paper.
#
# Pre-processing required
# -----------------------
# UAVid distributes 3-channel RGB colour-coded labels (Labels/ directory).
# Convert them to YOLO single-channel format first:
#
#   python src/scripts/convert_uavid_to_yolo.py \
#       --src /path/to/uavid \
#       --dst /path/to/uavid_yolo \
#       --info configs/UAVid_info.json \
#       --split both
#
# Then set the ``path`` field below to /path/to/uavid_yolo.
#
# Class mapping (all 8 classes are valid; none are ignored)
# -----------------------------------------------------------
#   0  Clutter       [  0,   0,   0]
#   1  Building      [128,   0,   0]
#   2  Road          [128,  64, 128]
#   3  Static Car    [192,   0, 192]
#   4  Tree          [  0, 128,   0]
#   5  Vegetation    [128, 128,   0]
#   6  Human         [ 64,  64,   0]
#   7  Moving Car    [ 64,   0, 128]

# Root path. Ultralytics parses this file with plain YAML (no Hydra/OmegaConf
# env-var resolution), so this must be a literal path — edit it directly for
# your machine, or point `data=` at a copy of this file with your own path.
path: /home/neural_debugger/Downloads/datasets/uavid_dataset/UAVid-YOLO

train: images/train # relative to path
val: images/val # relative to path
test: images/test # relative to path

# Mask directory (must mirror images/ structure): images/train -> masks/train, etc.
masks_dir: masks # relative to path

# Number of semantic classes (all 8 UAVid classes, Clutter included)
nc: 8

# Class names — order must match the integer class IDs above
names:
  0: Clutter
  1: Building
  2: Road
  3: StaticCar
  4: Tree
  5: Vegetation
  6: Human
  7: MovingCar

# Ultralytics training command example
# -------------------------------------
# yolo semantic train \
#     model=yolo26n-sem.pt \
#     data=configs/dataset/uavid_yolo.yaml \
#     imgsz=1024 \
#     epochs=100 \
#     batch=4 \
#     device=0