---
license: other
task_categories:
- image-segmentation
language:
- en
tags:
- image-segmentation
- semantic-segmentation
- computer-vision
- racetrack
- top-down
- binary-mask
- racelinecalc
pretty_name: RaceLineCalc Track Segmentation Dataset
size_categories:
- n<1K
configs:
- config_name: default
  drop_labels: true
---

# RaceLineCalc Track Segmentation Dataset

A small top-down racetrack segmentation dataset created for the automatic boundary detection feature in [RaceLineCalc](https://redratinhat.com/).

The companion model repository is [RedRatInHat/racelinecalc-track-mask](https://huggingface.co/RedRatInHat/racelinecalc-track-mask).

## Contents

- `raw/` - 120 RGB racetrack images.
- `labels/<sample_id>/target_track_mask.png` - binary track-surface masks.
- `labels/<sample_id>/overlay.png` - visual QA overlays.
- `labels/<sample_id>/label.json` - full freeform labeler annotation payloads.
- `manifests/` - source generation manifest.
- `prompts/` - generation prompt bundle used for the synthetic set.
- `reports/` - dataset preview and generation report.
- `metadata.csv` - ImageFolder-compatible table mapping each image to its mask, overlay and label JSON. The `*_file_name` columns let Hugging Face Dataset Viewer and `datasets.load_dataset(...)` decode `image`, `mask` and `overlay` as image columns.

## Loading

```python
from datasets import load_dataset

ds = load_dataset("RedRatInHat/racelinecalc-track-segmentation", split="train")
sample = ds[0]
image = sample["image"]
mask = sample["mask"]
overlay = sample["overlay"]
```

## Dataset Contract

Each sample is a binary semantic segmentation pair:

```text
input:  RGB top-down / satellite-like racetrack image
output: binary mask where positive pixels are drivable asphalt track surface
```

The dataset is intentionally narrow: it targets racetrack/asphalt surface extraction, not general road-scene segmentation.

## Splits

The split used for the published baseline model is available in the model repository under `training/split.json`.

## Related Model

The baseline mobile ONNX model trained on this dataset is published here:

[RedRatInHat/racelinecalc-track-mask](https://huggingface.co/RedRatInHat/racelinecalc-track-mask)

## Provenance

This dataset was generated and labeled during development of RaceLineCalc automatic track boundary detection. It is published as a full v1 research/portfolio dataset for reproducibility and external evaluation.

## License

License is marked as `other`. Commercial use of this dataset is not allowed without a separate written agreement with Red Rat In Hat.