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name: data-preparation description: "Prepares and validates BDD100K-style YOLOP data roots, detection JSONs, drivable-area masks, lane-line masks, and label-generation workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

YOLOP Data Preparation

Use this sub-skill when the task asks how to prepare BDD100K data for YOLOP, set DATASET.*ROOT config fields, diagnose missing masks/JSONs/images, generate drivable-area segmentation masks, or understand the detection label conversion used by BddDataset.

Do not use this sub-skill for model hyperparameters, checkpoint loading, ONNX export, or demo inference except where those tasks depend on data roots; route those to training, export, or inference.

Read first

• references/data-layout.md explains the expected BDD100K directory layout, how BddDataset maps mask paths to image/label/lane paths, and how detection categories are converted.
• references/troubleshooting.md covers empty datasets, missing path pairs, malformed JSONs, mask rendering failures, and common config mistakes.
• Run scripts/check_data_layout.py before training/evaluation to check that roots and split directories line up.
• Run scripts/generate_drivable_masks.py when you need a safer, argument-driven version of the repo's drivable-area mask generator.

Minimal workflow

1. Acquire the BDD100K images and the YOLOP-compatible detection, drivable-area, and lane-line annotations from the source project instructions or an already prepared mirror.
2. Put the roots into one of these forms:
   • README combined layout: images/{train,val}, det_annotations/{train,val}, da_seg_annotations/{train,val}, ll_seg_annotations/{train,val} under one dataset root.
   • Source config layout: independent roots where each root contains train/ and val/ subdirectories.
3. If drivable masks are not already PNG files, generate them from BDD polygon JSONs with the bundled generator.
4. Validate the roots and filename correspondence.
5. Update cfg.DATASET.DATAROOT, LABELROOT, MASKROOT, and LANEROOT before running training or evaluation.

Safe helpers

# Combined README-style root
python sub-skills/data-preparation/scripts/check_data_layout.py \
  --dataset-root /path/to/yolop-dataset --splits train val --max-samples 20

# Explicit roots matching lib/config/default.py fields
python sub-skills/data-preparation/scripts/check_data_layout.py \
  --images-root /path/to/images/100k \
  --det-root /path/to/det_annotations \
  --da-root /path/to/da_seg_annotations \
  --lane-root /path/to/ll_seg_annotations

# Generate drivable-area masks for one split from BDD label JSONs
python sub-skills/data-preparation/scripts/generate_drivable_masks.py \
  --labels-dir /path/to/bdd100k/labels/100k/train \
  --output-dir /path/to/bdd_seg_gt/train

These helpers do not import YOLOP source code. They validate/generate files from paths you pass explicitly and are safe to run outside the original repo checkout.

Key ownership boundary

This sub-skill owns data layout and label generation. Once the layout is valid, use ../training/SKILL.md for tools/train.py and tools/test.py config choices, metrics, and checkpoint behavior.