# Culane Eval

> Evaluates lane detection performance in diverse urban and highway scenarios using an F1-measure based on IoU between predicted and ground truth lane lines. Use when the user wants to benchmark on CULane, or asks about evaluating this task. Reports F1-measure.

- Skill: `qhjqhj00/culane-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/culane-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/culane-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/culane-eval

---


# culane-eval

> Ultra Fast Structure-aware Deep Lane Detection — Qin et al. (2020) (arXiv:2004.11757, 2020)

## What this evaluates

Evaluates lane detection performance in diverse urban and highway scenarios using an F1-measure based on IoU between predicted and ground truth lane lines.

## Datasets

- **CULane** — total 133235; splits: train (88880), validation (9675), test (34680)

## Metrics

- `F1-measure` **(primary)** — range: percent
  - F1 = 2 * Precision * Recall / (Precision + Recall). Each lane is treated as a 30-pixel-width line. IoU is computed between GT and prediction; IoU > 0.5 counts as True Positive.

## Input / output format

**Input**: RGB image (original resolution 1640x590, resized to 288x800 during training)

**Output**: Predicted lane lines/coordinates via row-anchor classification or regression

## Scoring recipe

```python
TP = FP = FN = 0
for clip in dataset:
    gt_lanes = get_gt_lanes(clip)
    pred_lanes = get_pred_lanes(clip)
    for gt in gt_lanes:
        best_iou = max(iou(gt, pred) for pred in pred_lanes)
        if best_iou > 0.5:
            TP += 1
            break
    else:
        FN += 1
    FP += len(pred_lanes) - TP
Precision = TP / (TP + FP) if (TP + FP) > 0 else 0
Recall = TP / (TP + FN) if (TP + FN) > 0 else 0
F1 = 2 * Precision * Recall / (Precision + Recall)
```

## Common pitfalls

- IoU threshold for positive match is strictly >0.5, not ≥0.5.
- Lanes are treated as 30-pixel-wide lines for IoU calculation, not 1-pixel curves.
- F1-measure is computed per clip, then averaged or summed across the dataset.

## Evidence (verbatim from paper)

> As for the evaluation metric of CULane, each lane is treated as a 30-pixel-width line. Then the intersection-over-union (IoU) is computed between ground truth and predictions. Predictions with IoUs larger than 0.5 are considered as true positives. F1-measure is taken as the evaluation metric and formulated as follows: F1-measure = 2*Precision*Recall/(Precision+Recall)

## Citation

```bibtex
@misc{qin2020ultrafast,
  title={Ultra Fast Structure-aware Deep Lane Detection},
  author={Qin et al. (2020)},
  year={2020},
  note={arXiv:2004.11757}
}
```

- arXiv: 2004.11757