| ---
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| license: cc-by-sa-4.0
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| task_categories:
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| - object-detection
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| - image-classification
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| language:
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| - en
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| tags:
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| - dice
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| - polyhedral-dice
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| - ttrpg
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| - tabletop
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| - object-detection
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| - webcam
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| size_categories:
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| - n<1K
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| pretty_name: dieCamera dice frames
|
| ---
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|
|
| # dieCamera — physical dice, read by webcam
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|
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| 351 webcam frames of physical polyhedral dice on a tray, with per-die **type**, **face
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| value** and **bounding box**. Collected to train the offline reader in
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| [dieCamera](https://github.com/eschatus/diecamera), an app that watches your dice tray
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| and posts the roll into a virtual tabletop.
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|
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| **1,079 labelled dice** across the six standard types (d4, d6, d8, d10, d12, d20).
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| 587 of those carry face values that are ground truth — confirmed by a human in the app,
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| or placed deliberately to a prompt. The rest are trustworthy for **type and box only**.
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| That distinction is a column, not a footnote — see *Label trust* below.
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|
|
| ## Why it is laid out by rig and date
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|
|
| ```
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| data/<camera>/<capture-date>/<frame-id>.jpg
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| data/metadata.jsonl
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| ```
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|
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| Both levels of that path are a domain boundary, and mixing across either is the main way
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| to get a misleading number out of this data.
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|
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| **Rig** is the obvious one: a gooseneck webcam over a gray tray, a phone camera over
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| felt, and a Nintendo Switch camera are three different problems. A model trained on one
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| and evaluated on another loses most of its apparent accuracy.
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|
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| **Date** is the one that cost us. The capture rig changed under the data. The app used to
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| ask the camera for 1080p and leave the lens wherever autofocus abandoned it; on
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| 2026-08-01 it started requesting full sensor resolution and sweeping the lens for the
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| sharpest focus. Same dice, same camera, same table — and the crop across a die went from
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| ~930px to ~1700px, with measured sharpness (variance of Laplacian at 224px) going 94 →
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| 294 → 326. Train across that boundary without knowing it is there and the model learns
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| the blur rather than the numeral.
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|
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| `metadata.jsonl` carries the exact timestamp and crop dimensions per frame, so any other
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| split — by sharpness, session, or lens era — is a filter away.
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|
|
| ## Contents
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|
|
| | rig | dates | frames | dice | trusted faces |
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| | ----------------------------------- | ----------------- | -----: | ---: | ------------: |
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| | `hue-hd-camera-0c45-6341` | 2026-07-10 → 07-15 | 105 | 336 | 198 |
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| | `android-webcam-18d1-4eed` | 2026-07-10 | 70 | 238 | 6 |
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| | `hd-usb-camera-05a3-9520` | 2026-07-19 → 08-01 | 89 | 225 | 206 |
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| | `unknown-rig` | 2026-07-09 → 07-10 | 65 | 219 | 116 |
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| | `triveni-s-iphone-2-camera` | 2026-07-15, 07-22 | 17 | 56 | 56 |
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| | `nintendo-switch-camera-057e-206d` | 2026-07-15 → 07-16 | 5 | 5 | 5 |
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|
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| `unknown-rig` is the earliest capture generation, from before the app recorded which
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| camera took a frame. It is believed to be the HUE gooseneck but the frames do not say so,
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| and guessing would defeat the point of splitting by rig.
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|
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| Frames are already cropped to the dice tray (the app's region-of-interest), which is why
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| image dimensions vary within a rig.
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|
|
| ## Fields
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|
|
| | field | meaning |
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| | ---------------- | ------------------------------------------------------------------------- |
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| | `file_name` | image path, relative to `data/` |
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| | `camera` | raw device label as the OS reported it |
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| | `epoch` | UTC capture date — the directory level above the frame |
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| | `captured_at` | full ISO timestamp |
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| | `width`,`height` | crop dimensions in pixels |
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| | `label_source` | `human` (confirmed in the app) or `teacher` (a batch vision-model pass) |
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| | `values_trusted` | whether the **face values** may be trained on |
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| | `dice` | `[{type, value, box:{x,y,w,h}, confidence}]`; boxes are frame fractions 0–1 |
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|
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| ```python
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| from datasets import load_dataset
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| ds = load_dataset("G-G-Games/diecamera-dice", split="train")
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|
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| # Faces are only safe to train on where the flag says so.
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| faces = ds.filter(lambda r: r["values_trusted"])
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|
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| # The sharp, full-resolution era on the current rig.
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| recent = ds.filter(lambda r: r["epoch"] >= "2026-08-01")
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| ```
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|
|
| ## Label trust
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|
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| Labels arrive by three routes, and they are not equally good:
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|
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| 1. **In-app confirmation.** Every roll the app reads goes through a correct-step where a
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| human clicks any die that was read wrong before it posts. What lands here is what a
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| person signed off on. Best quality; these are `label_source: human`.
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| 2. **Guided collection.** The app prompts for a specific set ("roll 2×d6 + 1×d20") and
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| captures on settle, so the prompted set *is* the type/count ground truth with no model
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| in the loop. In value-sweep mode the prompt names the exact faces to place, which makes
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| the values ground truth too.
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| 3. **Teacher passes.** A frontier vision model labelled the backlog. Usable for **types
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| and boxes**, which it gets right; its face reads are exactly what the local model
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| exists to replace, so they are never marked trusted.
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|
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| `values_trusted` encodes the outcome of that. **Type and box labels are usable on every
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| row; face values are only usable where `values_trusted` is true.** A model trained on the
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| untrusted faces is being trained on another model's guesses.
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|
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| ## Known issues
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|
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| - **d10 6-vs-9 is genuinely ambiguous** on some dice sets and is the single largest
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| source of face error. Where a set marks orientation with a dot or underline the label
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| follows the mark; ornate sets use a fleur-de-lis flourish, which is easy to mistake for
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| a mislabel and is not one.
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| - **Thin, but evenly thin.** All 60 (type, face) combinations have trusted labels, and
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| the rarest has 7 examples against the commonest's 20. Depth is the constraint, not
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| balance — 7 examples of a d12 showing 9 is not many pictures of a numeral.
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| - **One d100 die in the whole corpus**, so percentile is effectively uncovered.
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| - **`values_trusted` is false for most of `android-webcam-18d1-4eed`** — that generation
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| was type-labelled by guided collection and never had its faces confirmed.
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| - Boxes on human-confirmed frames originate from a model and were corrected only when
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| visibly wrong, so box tightness is not uniform.
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|
|
| ## Caveat on any accuracy number
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|
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| Everything published from this corpus so far was trained *and* evaluated on it with
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| splits that are not recorded here. Treat single-number accuracies with suspicion and cut
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| your own held-out split — by rig, or by date, so the test set is a domain the model has
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| not seen. In-domain depth, not corpus size, is the binding constraint on this problem.
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|
|
| ## Provenance and credit
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|
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| Captured by [@eschatus](https://github.com/eschatus) across five rigs, with frames
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| contributed by **[@trivenigandhi](https://github.com/trivenigandhi)** (the
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| `triveni-s-iphone-2-camera` rig). Labels are human confirmations plus teacher passes as
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| described above.
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|
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| Regenerate this layout from the source repo with `npm run dataset:export`.
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|
|
| ## License
|
|
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| **CC BY-SA 4.0.** Share-alike: any redistribution or derivative dataset built from this
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| corpus must carry attribution and the same license forward. The
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| [dieCamera](https://github.com/eschatus/diecamera) application code is licensed separately
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| (see its own repo).
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|
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| *(Provisional — this replaces an earlier CC BY 4.0 license on this card, to match the
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| "opt-in, copyleft" data-sharing terms agreed in principle on Aug 6. Not yet cleared by
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| counsel.)*
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|
