README.md

---
license: mit
task_categories:
- image-classification
tags:
- racing
- telemetry
- gear-detection
- motorsport
- digit-recognition
- onboard-camera
pretty_name: Racing Gear Digits
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: int64
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 3041614
    num_examples: 5470
  - name: validation
    num_bytes: 525445
    num_examples: 946
  download_size: 3435199
  dataset_size: 3567059
---

# Racing Gear Digits

Image classification dataset for detecting gear numbers (0-9) from racing onboard camera telemetry overlays, supplemented with MNIST digits for robustness.

Similar in spirit to MNIST but for a specific real-world application: reading the gear indicator from racing car onboard video feeds in real-time.

## Dataset

- **5,964 training** / **1,003 validation** images
- **32×32 grayscale** PNG images
- **10 classes** (digits 0-9)
- **Source column** distinguishes `racing-original`, `paul-ricard-alpine`, `sebring-tobi-lap6`, `racing_aug` (augmented), and `mnist`
- **Proper stratified split** — 15% of each racing source held out for validation
- **Augmented minority classes** — racing digits with <200 training samples augmented via random shifts, brightness/contrast jitter, and Gaussian noise

### Racing sources

| Source | Gears | Style |
|--------|-------|-------|
| TDS Racing IMSA Sebring 2026 (original) | 1-6 | White digit on gray RPM gauge face |
| Sebring Q Tobi Lap 6 | 1-6 | White digit on dark semi-transparent overlay |
| Paul Ricard Alpine LMPh | 1-7 | White digit on dark circle |
| MNIST supplement | 0-9 | Handwritten digits (generalization) |

### Train distribution

| Digit | Racing (orig) | Sebring | Paul Ricard | Aug | MNIST | Total |
|-------|--------------|---------|-------------|-----|-------|-------|
| 0 | 0 | 0 | 0 | 0 | 196 | 196 |
| 1 | 864 | 36 | 35 | 0 | 196 | 1,167 |
| 2 | 1,344 | 74 | 105 | 0 | 196 | 1,719 |
| 3 | 364 | 99 | 162 | 0 | 196 | 821 |
| 4 | 37 | 68 | 4 | 91 | 196 | 396 |
| 5 | 107 | 138 | 49 | 0 | 196 | 490 |
| 6 | 106 | 79 | 35 | 0 | 196 | 416 |
| 7 | 19 | 0 | 45 | 137 | 196 | 397 |
| 8 | 0 | 0 | 0 | 0 | 196 | 196 |
| 9 | 0 | 0 | 0 | 0 | 196 | 196 |

## Adding new video sources

1. **Extract** gear crops from a video:
   ```bash
   uv run python scripts/extract.py <video_path> <source_name> <x> <y> <w> <h>
   ```
   This creates `raw/<source>/unlabeled/` frames and a `composites/<source>/unlabeled.png` contact sheet.

2. **Label** by reading the contact sheet and creating `labels/<source>.csv`:
   ```csv
   start,end,label
   0,14,5
   15,39,6
   ```
   Each row maps a frame range (inclusive, 0-indexed) to a gear digit.

3. **Build** the dataset:
   ```bash
   uv run python scripts/build_dataset.py
   ```
   This reads all `raw/` sources and `labels/` CSVs, does stratified train/val splitting, augments minority classes, and writes the parquet files.

## Augmentations (racing_aug)

For racing classes with fewer than 200 training samples, synthetic samples are generated:

- **Random translation** — up to ±3px shift in x/y
- **Brightness jitter** — 0.7–1.3×
- **Contrast jitter** — 0.8–1.2×
- **Gaussian noise** — σ=8, 30% probability

## Usage

```python
from datasets import load_dataset

ds = load_dataset("tobil/racing-gears")

# Filter to real racing images only
racing = ds["train"].filter(lambda x: x["source"] not in ("mnist", "racing_aug"))

# Standard training loop
for example in ds["train"]:
    image = example["image"]   # PIL Image, 32x32 grayscale
    label = example["label"]   # int 0-9
    source = example["source"] # source identifier
```

## Context

Built for the [mpv racing telemetry plugin](https://github.com/tobi/mpv-telemetry) which reads baked-in telemetry from racing onboard videos using mpv's `screenshot-raw` API and renders a live overlay with throttle/brake traces, gear indicator, and steering position.

The gear digit is detected using a small CNN (ONNX) called via LuaJIT FFI from mpv's Lua scripting environment at 30fps.