G-G-Games/diecamera-dice

dieCamera — physical dice, read by webcam

351 webcam frames of physical polyhedral dice on a tray, with per-die type, face value and bounding box. Collected to train the offline reader in dieCamera, an app that watches your dice tray and posts the roll into a virtual tabletop.

1,079 labelled dice across the six standard types (d4, d6, d8, d10, d12, d20). 587 of those carry face values that are ground truth — confirmed by a human in the app, or placed deliberately to a prompt. The rest are trustworthy for type and box only. That distinction is a column, not a footnote — see Label trust below.

Why it is laid out by rig and date

data/<camera>/<capture-date>/<frame-id>.jpg
data/metadata.jsonl

Both levels of that path are a domain boundary, and mixing across either is the main way to get a misleading number out of this data.

Rig is the obvious one: a gooseneck webcam over a gray tray, a phone camera over felt, and a Nintendo Switch camera are three different problems. A model trained on one and evaluated on another loses most of its apparent accuracy.

Date is the one that cost us. The capture rig changed under the data. The app used to ask the camera for 1080p and leave the lens wherever autofocus abandoned it; on 2026-08-01 it started requesting full sensor resolution and sweeping the lens for the sharpest focus. Same dice, same camera, same table — and the crop across a die went from ~930px to ~1700px, with measured sharpness (variance of Laplacian at 224px) going 94 → 294 → 326. Train across that boundary without knowing it is there and the model learns the blur rather than the numeral.

metadata.jsonl carries the exact timestamp and crop dimensions per frame, so any other split — by sharpness, session, or lens era — is a filter away.

Contents

rig	dates	frames	dice	trusted faces
hue-hd-camera-0c45-6341	2026-07-10 → 07-15	105	336	198
android-webcam-18d1-4eed	2026-07-10	70	238	6
hd-usb-camera-05a3-9520	2026-07-19 → 08-01	89	225	206
unknown-rig	2026-07-09 → 07-10	65	219	116
triveni-s-iphone-2-camera	2026-07-15, 07-22	17	56	56
nintendo-switch-camera-057e-206d	2026-07-15 → 07-16	5	5	5

unknown-rig is the earliest capture generation, from before the app recorded which camera took a frame. It is believed to be the HUE gooseneck but the frames do not say so, and guessing would defeat the point of splitting by rig.

Frames are already cropped to the dice tray (the app's region-of-interest), which is why image dimensions vary within a rig.

Fields

field	meaning
file_name	image path, relative to data/
camera	raw device label as the OS reported it
epoch	UTC capture date — the directory level above the frame
captured_at	full ISO timestamp
width,height	crop dimensions in pixels
label_source	human (confirmed in the app) or teacher (a batch vision-model pass)
values_trusted	whether the face values may be trained on
dice	[{type, value, box:{x,y,w,h}, confidence}]; boxes are frame fractions 0–1

from datasets import load_dataset
ds = load_dataset("G-G-Games/diecamera-dice", split="train")

# Faces are only safe to train on where the flag says so.
faces = ds.filter(lambda r: r["values_trusted"])

# The sharp, full-resolution era on the current rig.
recent = ds.filter(lambda r: r["epoch"] >= "2026-08-01")

Label trust

Labels arrive by three routes, and they are not equally good:

1. In-app confirmation. Every roll the app reads goes through a correct-step where a human clicks any die that was read wrong before it posts. What lands here is what a person signed off on. Best quality; these are label_source: human.
2. Guided collection. The app prompts for a specific set ("roll 2×d6 + 1×d20") and captures on settle, so the prompted set is the type/count ground truth with no model in the loop. In value-sweep mode the prompt names the exact faces to place, which makes the values ground truth too.
3. Teacher passes. A frontier vision model labelled the backlog. Usable for types and boxes, which it gets right; its face reads are exactly what the local model exists to replace, so they are never marked trusted.

values_trusted encodes the outcome of that. Type and box labels are usable on every row; face values are only usable where values_trusted is true. A model trained on the untrusted faces is being trained on another model's guesses.

Known issues

• d10 6-vs-9 is genuinely ambiguous on some dice sets and is the single largest source of face error. Where a set marks orientation with a dot or underline the label follows the mark; ornate sets use a fleur-de-lis flourish, which is easy to mistake for a mislabel and is not one.
• Thin, but evenly thin. All 60 (type, face) combinations have trusted labels, and the rarest has 7 examples against the commonest's 20. Depth is the constraint, not balance — 7 examples of a d12 showing 9 is not many pictures of a numeral.
• One d100 die in the whole corpus, so percentile is effectively uncovered.
• values_trusted is false for most of android-webcam-18d1-4eed — that generation was type-labelled by guided collection and never had its faces confirmed.
• Boxes on human-confirmed frames originate from a model and were corrected only when visibly wrong, so box tightness is not uniform.

Caveat on any accuracy number

Everything published from this corpus so far was trained and evaluated on it with splits that are not recorded here. Treat single-number accuracies with suspicion and cut your own held-out split — by rig, or by date, so the test set is a domain the model has not seen. In-domain depth, not corpus size, is the binding constraint on this problem.

Provenance and credit

Captured by @eschatus across five rigs, with frames contributed by @trivenigandhi (the triveni-s-iphone-2-camera rig). Labels are human confirmations plus teacher passes as described above.

Regenerate this layout from the source repo with npm run dataset:export.

License

CC BY-SA 4.0. Share-alike: any redistribution or derivative dataset built from this corpus must carry attribution and the same license forward. The dieCamera application code is licensed separately (see its own repo).

(Provisional — this replaces an earlier CC BY 4.0 license on this card, to match the "opt-in, copyleft" data-sharing terms agreed in principle on Aug 6. Not yet cleared by counsel.)