| ---
|
| license: other
|
| task_categories:
|
| - image-segmentation
|
| language:
|
| - en
|
| tags:
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| - 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.
|
|
|
